Overview

Brought to you by YData

Dataset statistics

Number of variables134
Number of observations500
Missing cells14085
Missing cells (%)21.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory523.6 KiB
Average record size in memory1.0 KiB

Variable types

Text19
Categorical106
Numeric5
Unsupported4

Alerts

CWSU-12 - Issues (Bottled Water) - If yes, select all that apply has constant value "delivery during wet season" Constant
CWSU-12 - Issues (Bottled Water) - Turbidity has constant value "0" Constant
AWTP - Max Price - Commercial - 100 is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
AWTP - Max Price - Commercial - 160 is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
AWTP - Max Price - Commercial - 200 is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
AWTP-14 - Factors affecting Alt Sources - Cost-effectiveness is highly overall correlated with AWTP-14 - Factors affecting Alt Sources - Costumer service and maintenance supportHigh correlation
AWTP-14 - Factors affecting Alt Sources - Costumer service and maintenance support is highly overall correlated with AWTP-14 - Factors affecting Alt Sources - Cost-effectiveness and 1 other fieldsHigh correlation
AWTP-14 - Factors affecting Alt Sources - Others is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 3 other fieldsHigh correlation
AWTP-15 - Desalination Awareness - No is highly overall correlated with AWTP-15 - Desalination Awareness - YesHigh correlation
AWTP-15 - Desalination Awareness - Yes is highly overall correlated with AWTP-15 - Desalination Awareness - NoHigh correlation
AWTP-16 - Desalinated Willingness - No is highly overall correlated with AWTP-16 - Desalinated Willingness - YesHigh correlation
AWTP-16 - Desalinated Willingness - Yes is highly overall correlated with AWTP-16 - Desalinated Willingness - NoHigh correlation
AWTP-17 - Desalinated Premium Pay - Fixed tariff is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
AWTP-17 - Desalinated Premium Pay - Pay per use is highly overall correlated with AWTP-19 - Max Price - Residential - OthersHigh correlation
AWTP-17 - Desalinated Premium Pay - Seasonal is highly overall correlated with AWTP-20 - Budget - Others and 3 other fieldsHigh correlation
AWTP-17 - Desalinated Premium Pay - Yes is highly overall correlated with AWTP-19 - Max Price - Residential - OthersHigh correlation
AWTP-19 - Max Price - Residential - 100 is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 14 other fieldsHigh correlation
AWTP-19 - Max Price - Residential - 50 is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 15 other fieldsHigh correlation
AWTP-19 - Max Price - Residential - 80 is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 14 other fieldsHigh correlation
AWTP-19 - Max Price - Residential - Others is highly overall correlated with AWTP - Max Price - Commercial - 100 and 58 other fieldsHigh correlation
AWTP-20 - Budget - 3000 - 6000 is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 6 other fieldsHigh correlation
AWTP-20 - Budget - 6000 - 9000 is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 6 other fieldsHigh correlation
AWTP-20 - Budget - 9000 - 15000 is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 7 other fieldsHigh correlation
AWTP-20 - Budget - Others is highly overall correlated with AWTP-14 - Factors affecting Alt Sources - Costumer service and maintenance support and 45 other fieldsHigh correlation
Barangay is highly overall correlated with AWTP - Max Price - Commercial - 160 and 10 other fieldsHigh correlation
CWSU-1 - Usage - Cleaning & Sanitation is highly overall correlated with GI - Business Location/Address and 1 other fieldsHigh correlation
CWSU-1 - Usage - Food preparation is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
CWSU-1 - Usage - Landscaping/Irrigation is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
CWSU-1 - Usage - Others is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 7 other fieldsHigh correlation
CWSU-10 - Issues (Deep Well) - Costs is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 8 other fieldsHigh correlation
CWSU-10 - Issues (Deep Well) - N/A is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
CWSU-10 - Issues (Deep Well) - No is highly overall correlated with CWSU-10 - Issues (Deep Well) - YesHigh correlation
CWSU-10 - Issues (Deep Well) - Others is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
CWSU-10 - Issues (Deep Well) - Salinity is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 3 other fieldsHigh correlation
CWSU-10 - Issues (Deep Well) - Smell is highly overall correlated with AWTP-20 - Budget - Others and 1 other fieldsHigh correlation
CWSU-10 - Issues (Deep Well) - Supply interruption is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
CWSU-10 - Issues (Deep Well) - Taste is highly overall correlated with AWTP-20 - Budget - OthersHigh correlation
CWSU-10 - Issues (Deep Well) - Turbidity is highly overall correlated with AWTP-20 - Budget - Others and 2 other fieldsHigh correlation
CWSU-10 - Issues (Deep Well) - Yes is highly overall correlated with CWSU-10 - Issues (Deep Well) - No and 1 other fieldsHigh correlation
CWSU-11 - Issues (Truck Water) - Costs is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 3 other fieldsHigh correlation
CWSU-11 - Issues (Truck Water) - N/A is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 4 other fieldsHigh correlation
CWSU-11 - Issues (Truck Water) - No is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 3 other fieldsHigh correlation
CWSU-11 - Issues (Truck Water) - Others is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 5 other fieldsHigh correlation
CWSU-11 - Issues (Truck Water) - Salinity is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 3 other fieldsHigh correlation
CWSU-11 - Issues (Truck Water) - Smell is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 6 other fieldsHigh correlation
CWSU-11 - Issues (Truck Water) - Supply interruption is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 7 other fieldsHigh correlation
CWSU-11 - Issues (Truck Water) - Taste is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 3 other fieldsHigh correlation
CWSU-11 - Issues (Truck Water) - Turbidity is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 9 other fieldsHigh correlation
CWSU-11 - Issues (Truck Water) - Yes is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
CWSU-12 - Issues (Bottled Water) - Costs is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
CWSU-12 - Issues (Bottled Water) - N/A is highly overall correlated with CWSU-12 - Issues (Bottled Water) - NoHigh correlation
CWSU-12 - Issues (Bottled Water) - No is highly overall correlated with CWSU-12 - Issues (Bottled Water) - N/AHigh correlation
CWSU-12 - Issues (Bottled Water) - Others is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 3 other fieldsHigh correlation
CWSU-12 - Issues (Bottled Water) - Salinity is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 10 other fieldsHigh correlation
CWSU-12 - Issues (Bottled Water) - Smell is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 9 other fieldsHigh correlation
CWSU-12 - Issues (Bottled Water) - Supply interruption is highly overall correlated with AWTP-20 - Budget - Others and 2 other fieldsHigh correlation
CWSU-12 - Issues (Bottled Water) - Taste is highly overall correlated with AWTP-20 - Budget - Others and 2 other fieldsHigh correlation
CWSU-12 - Issues (Bottled Water) - Yes is highly overall correlated with AWTP-20 - Budget - Others and 1 other fieldsHigh correlation
CWSU-13 - Interruptions (days) - Thrice a week is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
CWSU-13 - Interruptions (days) - Twice a week is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
CWSU-13 - Interruptions (days) - We have continuous water supply everyday is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 3 other fieldsHigh correlation
CWSU-2 - Treatment is highly overall correlated with BarangayHigh correlation
CWSU-3 - Current Sources - Deep Well (owned) is highly overall correlated with CWSU-8 - Monthly Costs - Truck DeliveryHigh correlation
CWSU-3 - Current Sources - Others is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 8 other fieldsHigh correlation
CWSU-3 - Current Sources - Truck Delivery Services (5 m3) is highly overall correlated with CWSU-6 - Actual Use (m3 / gal) - Tap Water (Water District etc.) and 1 other fieldsHigh correlation
CWSU-4 - Primary Drinking Source - Deep Well (owned) is highly overall correlated with AWTP-19 - Max Price - Residential - OthersHigh correlation
CWSU-4 - Primary Drinking Source - Others is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
CWSU-4 - Primary Drinking Source - Truck Delivery Services (5 m3) is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
CWSU-4 - Primary Drinking Source - gals is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
CWSU-5 - Ave Demand (in cbm) is highly overall correlated with AWTP-20 - Budget - Others and 6 other fieldsHigh correlation
CWSU-6 - Actual Use (m3 / gal) - Deep Well (owned) is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 14 other fieldsHigh correlation
CWSU-6 - Actual Use (m3 / gal) - Tap Water (Water District etc.) is highly overall correlated with AWTP - Max Price - Commercial - 100 and 33 other fieldsHigh correlation
CWSU-7 - Peak Month - End is highly overall correlated with CWSU-10 - Issues (Deep Well) - Costs and 1 other fieldsHigh correlation
CWSU-7 - Peak Month - Start is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 3 other fieldsHigh correlation
CWSU-8 - Monthly Costs - Less than PhP 1,000 is highly overall correlated with AWTP-19 - Max Price - Residential - OthersHigh correlation
CWSU-8 - Monthly Costs - Others: is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 6 other fieldsHigh correlation
CWSU-8 - Monthly Costs - PhP 10,000 and above is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
CWSU-8 - Monthly Costs - PhP 3,000 - PhP 5,000 is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
CWSU-8 - Monthly Costs - PhP 5,000 - PhP 10,000 is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
CWSU-8 - Monthly Costs - Truck Delivery is highly overall correlated with AWTP - Max Price - Commercial - 100 and 42 other fieldsHigh correlation
CWSU-9 - Issues (Tap Water) - N/A is highly overall correlated with CWSU-6 - Actual Use (m3 / gal) - Tap Water (Water District etc.)High correlation
CWSU-9 - Issues (Tap Water) - Others is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 4 other fieldsHigh correlation
CWSU-9 - Issues (Tap Water) - Salinity is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
CWSU-9 - Issues (Tap Water) - Smell is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
CWSU-9 - Issues (Tap Water) - Supply interruption is highly overall correlated with CWSU-9 - Issues (Tap Water) - YesHigh correlation
CWSU-9 - Issues (Tap Water) - Taste is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 1 other fieldsHigh correlation
CWSU-9 - Issues (Tap Water) - Turbidity is highly overall correlated with AWTP-19 - Max Price - Residential - Others and 2 other fieldsHigh correlation
CWSU-9 - Issues (Tap Water) - Yes is highly overall correlated with CWSU-9 - Issues (Tap Water) - Supply interruptionHigh correlation
Classification is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 15 other fieldsHigh correlation
GI - Business Location/Address is highly overall correlated with AWTP-17 - Desalinated Premium Pay - Seasonal and 13 other fieldsHigh correlation
GI - Date is highly overall correlated with Barangay and 3 other fieldsHigh correlation
GI - Interviewer is highly overall correlated with AWTP-17 - Desalinated Premium Pay - Seasonal and 8 other fieldsHigh correlation
GI - Members is highly overall correlated with AWTP-20 - Budget - 3000 - 6000 and 11 other fieldsHigh correlation
GI - Operating Hours - End is highly overall correlated with CWSU-11 - Issues (Truck Water) - Turbidity and 2 other fieldsHigh correlation
GI - Operating Hours - Start is highly overall correlated with CWSU-11 - Issues (Truck Water) - Turbidity and 3 other fieldsHigh correlation
GI - Remarks (Small, Medium, Large, Residential) is highly overall correlated with AWTP-19 - Max Price - Residential - 100 and 7 other fieldsHigh correlation
GI - Type of Business is highly overall correlated with AWTP-20 - Budget - Others and 7 other fieldsHigh correlation
GI - Operating Hours - Start is highly imbalanced (50.9%) Imbalance
GI - Operating Hours - End is highly imbalanced (52.9%) Imbalance
CWSU-1 - Usage - Landscaping/Irrigation is highly imbalanced (74.8%) Imbalance
CWSU-1 - Usage - Others is highly imbalanced (94.7%) Imbalance
CWSU-3 - Current Sources - Others is highly imbalanced (89.4%) Imbalance
CWSU-4 - Primary Drinking Source - Tap Water (Water District etc.) is highly imbalanced (60.5%) Imbalance
CWSU-4 - Primary Drinking Source - Truck Delivery Services (5 m3) is highly imbalanced (68.9%) Imbalance
CWSU-4 - Primary Drinking Source - Others is highly imbalanced (82.6%) Imbalance
CWSU-8 - Monthly Costs - PhP 3,000 - PhP 5,000 is highly imbalanced (63.4%) Imbalance
CWSU-8 - Monthly Costs - PhP 5,000 - PhP 10,000 is highly imbalanced (76.7%) Imbalance
CWSU-8 - Monthly Costs - PhP 10,000 and above is highly imbalanced (74.0%) Imbalance
CWSU-8 - Monthly Costs - Truck Delivery is highly imbalanced (67.1%) Imbalance
CWSU-8 - Monthly Costs - Others: is highly imbalanced (82.6%) Imbalance
CWSU-9 - Issues (Tap Water) - Salinity is highly imbalanced (84.7%) Imbalance
CWSU-9 - Issues (Tap Water) - Taste is highly imbalanced (82.6%) Imbalance
CWSU-9 - Issues (Tap Water) - Smell is highly imbalanced (77.6%) Imbalance
CWSU-9 - Issues (Tap Water) - Turbidity is highly imbalanced (91.9%) Imbalance
CWSU-9 - Issues (Tap Water) - Others is highly imbalanced (91.9%) Imbalance
CWSU-10 - Issues (Deep Well) - Supply interruption is highly imbalanced (59.1%) Imbalance
CWSU-10 - Issues (Deep Well) - Salinity is highly imbalanced (84.7%) Imbalance
CWSU-10 - Issues (Deep Well) - Taste is highly imbalanced (72.2%) Imbalance
CWSU-10 - Issues (Deep Well) - Smell is highly imbalanced (71.4%) Imbalance
CWSU-10 - Issues (Deep Well) - Turbidity is highly imbalanced (78.6%) Imbalance
CWSU-10 - Issues (Deep Well) - Costs is highly imbalanced (96.2%) Imbalance
CWSU-10 - Issues (Deep Well) - Others is highly imbalanced (79.6%) Imbalance
CWSU-11 - Issues (Truck Water) - Yes is highly imbalanced (74.9%) Imbalance
CWSU-11 - Issues (Truck Water) - Supply interruption is highly imbalanced (89.4%) Imbalance
CWSU-11 - Issues (Truck Water) - Salinity is highly imbalanced (93.3%) Imbalance
CWSU-11 - Issues (Truck Water) - Taste is highly imbalanced (93.3%) Imbalance
CWSU-11 - Issues (Truck Water) - Smell is highly imbalanced (94.7%) Imbalance
CWSU-11 - Issues (Truck Water) - Turbidity is highly imbalanced (96.2%) Imbalance
CWSU-11 - Issues (Truck Water) - Costs is highly imbalanced (89.4%) Imbalance
CWSU-11 - Issues (Truck Water) - Others is highly imbalanced (93.3%) Imbalance
CWSU-12 - Issues (Bottled Water) - Yes is highly imbalanced (59.1%) Imbalance
CWSU-12 - Issues (Bottled Water) - N/A is highly imbalanced (50.0%) Imbalance
CWSU-12 - Issues (Bottled Water) - Supply interruption is highly imbalanced (87.0%) Imbalance
CWSU-12 - Issues (Bottled Water) - Salinity is highly imbalanced (96.2%) Imbalance
CWSU-12 - Issues (Bottled Water) - Taste is highly imbalanced (76.7%) Imbalance
CWSU-12 - Issues (Bottled Water) - Smell is highly imbalanced (94.7%) Imbalance
CWSU-12 - Issues (Bottled Water) - Costs is highly imbalanced (85.9%) Imbalance
CWSU-12 - Issues (Bottled Water) - Others is highly imbalanced (89.4%) Imbalance
CWSU-13 - Interruptions (days) - Once a week is highly imbalanced (64.9%) Imbalance
CWSU-13 - Interruptions (days) - Twice a week is highly imbalanced (75.8%) Imbalance
CWSU-13 - Interruptions (days) - Thrice a week is highly imbalanced (87.0%) Imbalance
AWTP-14 - Factors affecting Alt Sources - Others is highly imbalanced (87.0%) Imbalance
AWTP-17 - Desalinated Premium Pay - Fixed tariff is highly imbalanced (74.0%) Imbalance
AWTP-17 - Desalinated Premium Pay - Seasonal is highly imbalanced (72.2%) Imbalance
AWTP - Max Price - Commercial - 200 is highly imbalanced (56.4%) Imbalance
AWTP - Max Price - Commercial - 100 is highly imbalanced (64.2%) Imbalance
AWTP-19 - Max Price - Residential - 80 is highly imbalanced (65.6%) Imbalance
AWTP-20 - Budget - 3000 - 6000 is highly imbalanced (76.7%) Imbalance
AWTP-20 - Budget - 6000 - 9000 is highly imbalanced (85.9%) Imbalance
AWTP-20 - Budget - 9000 - 15000 is highly imbalanced (88.2%) Imbalance
GI - Interviewer has 124 (24.8%) missing values Missing
GI - Remarks (Small, Medium, Large, Residential) has 31 (6.2%) missing values Missing
GI - Business Name/Residential Name/Contact number has 42 (8.4%) missing values Missing
GI - Business Location/Address has 112 (22.4%) missing values Missing
GI - Type of Business has 197 (39.4%) missing values Missing
GI - No. of Employees has 251 (50.2%) missing values Missing
GI - Operating Hours - Start has 178 (35.6%) missing values Missing
GI - Operating Hours - End has 178 (35.6%) missing values Missing
CWSU-1 - Usage - Specify has 498 (99.6%) missing values Missing
GI - Members has 389 (77.8%) missing values Missing
CWSU-3 - Current Sources - (If others, please specify) has 491 (98.2%) missing values Missing
CWSU-4 - Primary Drinking Source - (If others, please specify) has 486 (97.2%) missing values Missing
CWSU-5 - Ave Demand (in cbm) has 286 (57.2%) missing values Missing
CWSU-6 - Actual Use (m3 / gal) - Tap Water (Water District etc.) has 458 (91.6%) missing values Missing
CWSU-6 - Actual Use (m3 / gal) - Deep Well (owned) has 354 (70.8%) missing values Missing
CWSU-6 - Actual Use (m3 / gal) - Truck Delivery Services (5 m3) has 464 (92.8%) missing values Missing
CWSU-6 - Actual Use (m3 / gal) - Bottled water (5 gallons) has 222 (44.4%) missing values Missing
CWSU-6 - Actual Use (m3 / gal) - Others (gallons) has 488 (97.6%) missing values Missing
CWSU-7 - Peak Month - Start has 151 (30.2%) missing values Missing
CWSU-7 - Peak Month - End has 151 (30.2%) missing values Missing
CWSU-8 - Monthly Costs - Water Bottles has 376 (75.2%) missing values Missing
CWSU-8 - Monthly Costs - Truck Delivery has 425 (85.0%) missing values Missing
CWSU-8 - Monthly Costs - If others, input here has 488 (97.6%) missing values Missing
CWSU-9 - Issues (Tap Water) - If others, input here has 492 (98.4%) missing values Missing
CWSU-10 - Issues (Deep Well) - If others, input here has 484 (96.8%) missing values Missing
CWSU-11 - Issues (Truck Water) - If others, input here has 496 (99.2%) missing values Missing
CWSU-12 - Issues (Bottled Water) - If yes, select all that apply has 499 (99.8%) missing values Missing
CWSU-12 - Issues (Bottled Water) - If others, input here has 494 (98.8%) missing values Missing
CWSU-13 - Interruptions (days) - Others has 457 (91.4%) missing values Missing
CWSU-13 - Interruptions (days) - How many hours per day do you experience water interruption? has 459 (91.8%) missing values Missing
AWTP-14 - Factors affecting Alt Sources - If others, input here has 497 (99.4%) missing values Missing
AWTP-17 - Desalinated Premium Pay - If yes, in what payment structure has 500 (100.0%) missing values Missing
AWTP-19 - Max Price - Commercial - Others has 477 (95.4%) missing values Missing
AWTP-19 - Max Price - Residential - 50 has 360 (72.0%) missing values Missing
AWTP-19 - Max Price - Residential - 80 has 360 (72.0%) missing values Missing
AWTP-19 - Max Price - Residential - 100 has 360 (72.0%) missing values Missing
AWTP-19 - Max Price - Residential - Others has 488 (97.6%) missing values Missing
AWTP-20 - Budget - Others has 450 (90.0%) missing values Missing
AWTP-21 - Concerns has 363 (72.6%) missing values Missing
CWSU-6 - Actual Use (m3 / gal) - Truck Delivery Services (5 m3) is an unsupported type, check if it needs cleaning or further analysis Unsupported
CWSU-6 - Actual Use (m3 / gal) - Bottled water (5 gallons) is an unsupported type, check if it needs cleaning or further analysis Unsupported
CWSU-9 - Issues (Tap Water) - Costs is an unsupported type, check if it needs cleaning or further analysis Unsupported
AWTP-17 - Desalinated Premium Pay - If yes, in what payment structure is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-07-05 17:34:12.326990
Analysis finished2025-07-05 17:35:59.030409
Duration1 minute and 46.7 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2025-07-05T17:35:59.453483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.62
Min length5

Characters and Unicode

Total characters3310
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique494 ?
Unique (%)98.8%

Sample

1st rowBI-1R
2nd rowBI-2R
3rd rowBI-3R
4th rowBI-4R
5th rowBI-5R
ValueCountFrequency (%)
biii-51c 2
 
0.4%
bii-12c 2
 
0.4%
biii-54c 2
 
0.4%
bi-2r 1
 
0.2%
tan-1c 1
 
0.2%
sev-53c 1
 
0.2%
sev-54c 1
 
0.2%
sev-55c 1
 
0.2%
sev-56c 1
 
0.2%
sev-57c 1
 
0.2%
Other values (487) 487
97.4%
2025-07-05T17:36:00.003047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 500
15.1%
C 438
13.2%
I 305
 
9.2%
R 241
 
7.3%
A 202
 
6.1%
1 177
 
5.3%
B 145
 
4.4%
V 110
 
3.3%
2 99
 
3.0%
3 94
 
2.8%
Other values (17) 999
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 500
15.1%
C 438
13.2%
I 305
 
9.2%
R 241
 
7.3%
A 202
 
6.1%
1 177
 
5.3%
B 145
 
4.4%
V 110
 
3.3%
2 99
 
3.0%
3 94
 
2.8%
Other values (17) 999
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 500
15.1%
C 438
13.2%
I 305
 
9.2%
R 241
 
7.3%
A 202
 
6.1%
1 177
 
5.3%
B 145
 
4.4%
V 110
 
3.3%
2 99
 
3.0%
3 94
 
2.8%
Other values (17) 999
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 500
15.1%
C 438
13.2%
I 305
 
9.2%
R 241
 
7.3%
A 202
 
6.1%
1 177
 
5.3%
B 145
 
4.4%
V 110
 
3.3%
2 99
 
3.0%
3 94
 
2.8%
Other values (17) 999
30.2%

Barangay
Categorical

High correlation 

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
SEVILLA
67 
BARANGAY III
57 
CARLATAN
57 
LINGSAT
39 
CATBANGEN
32 
Other values (19)
248 

Length

Max length15
Median length11
Mean length8.714
Min length4

Characters and Unicode

Total characters4357
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBARANGAY I
2nd rowBARANGAY I
3rd rowBARANGAY I
4th rowBARANGAY I
5th rowBARANGAY I

Common Values

ValueCountFrequency (%)
SEVILLA 67
13.4%
BARANGAY III 57
 
11.4%
CARLATAN 57
 
11.4%
LINGSAT 39
 
7.8%
CATBANGEN 32
 
6.4%
BARANGAY I 31
 
6.2%
BARANGAY IV 23
 
4.6%
PORO 17
 
3.4%
PAGDARAOAN 17
 
3.4%
TANQUI 16
 
3.2%
Other values (14) 144
28.8%

Length

2025-07-05T17:36:00.155162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
barangay 135
20.5%
sevilla 77
11.7%
iii 57
8.6%
carlatan 57
8.6%
lingsat 49
 
7.4%
iv 33
 
5.0%
catbangen 32
 
4.9%
i 31
 
4.7%
poro 27
 
4.1%
pagdaraoan 27
 
4.1%
Other values (8) 134
20.3%

Most occurring characters

ValueCountFrequency (%)
A 900
20.7%
I 401
 
9.2%
N 348
 
8.0%
G 255
 
5.9%
L 240
 
5.5%
R 238
 
5.5%
169
 
3.9%
T 168
 
3.9%
B 167
 
3.8%
Y 149
 
3.4%
Other values (26) 1322
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 900
20.7%
I 401
 
9.2%
N 348
 
8.0%
G 255
 
5.9%
L 240
 
5.5%
R 238
 
5.5%
169
 
3.9%
T 168
 
3.9%
B 167
 
3.8%
Y 149
 
3.4%
Other values (26) 1322
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 900
20.7%
I 401
 
9.2%
N 348
 
8.0%
G 255
 
5.9%
L 240
 
5.5%
R 238
 
5.5%
169
 
3.9%
T 168
 
3.9%
B 167
 
3.8%
Y 149
 
3.4%
Other values (26) 1322
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 900
20.7%
I 401
 
9.2%
N 348
 
8.0%
G 255
 
5.9%
L 240
 
5.5%
R 238
 
5.5%
169
 
3.9%
T 168
 
3.9%
B 167
 
3.8%
Y 149
 
3.4%
Other values (26) 1322
30.3%

Classification
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Commercial
360 
Residential
140 

Length

Max length11
Median length10
Mean length10.28
Min length10

Characters and Unicode

Total characters5140
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResidential
2nd rowResidential
3rd rowResidential
4th rowResidential
5th rowResidential

Common Values

ValueCountFrequency (%)
Commercial 360
72.0%
Residential 140
 
28.0%

Length

2025-07-05T17:36:01.326488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:01.846658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
commercial 360
72.0%
residential 140
 
28.0%

Most occurring characters

ValueCountFrequency (%)
m 720
14.0%
e 640
12.5%
i 640
12.5%
a 500
9.7%
l 500
9.7%
o 360
7.0%
c 360
7.0%
r 360
7.0%
C 360
7.0%
R 140
 
2.7%
Other values (4) 560
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 720
14.0%
e 640
12.5%
i 640
12.5%
a 500
9.7%
l 500
9.7%
o 360
7.0%
c 360
7.0%
r 360
7.0%
C 360
7.0%
R 140
 
2.7%
Other values (4) 560
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 720
14.0%
e 640
12.5%
i 640
12.5%
a 500
9.7%
l 500
9.7%
o 360
7.0%
c 360
7.0%
r 360
7.0%
C 360
7.0%
R 140
 
2.7%
Other values (4) 560
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 720
14.0%
e 640
12.5%
i 640
12.5%
a 500
9.7%
l 500
9.7%
o 360
7.0%
c 360
7.0%
r 360
7.0%
C 360
7.0%
R 140
 
2.7%
Other values (4) 560
10.9%

GI - Date
Categorical

High correlation 

Distinct12
Distinct (%)2.4%
Missing5
Missing (%)1.0%
Memory size4.0 KiB
06/06/2025
120 
06/07/2025
88 
06/10/2025
86 
06/09/2025
77 
07/06/2025
53 
Other values (7)
71 

Length

Max length10
Median length10
Mean length9.9939394
Min length7

Characters and Unicode

Total characters4947
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row07/06/2025
2nd row07/06/2025
3rd row07/06/2025
4th row07/06/2025
5th row07/06/2025

Common Values

ValueCountFrequency (%)
06/06/2025 120
24.0%
06/07/2025 88
17.6%
06/10/2025 86
17.2%
06/09/2025 77
15.4%
07/06/2025 53
10.6%
09/06/2025 18
 
3.6%
10/06/2025 18
 
3.6%
06/08/2025 13
 
2.6%
11/06/2025 10
 
2.0%
08/06/2025 6
 
1.2%
Other values (2) 6
 
1.2%

Length

2025-07-05T17:36:01.956202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06/06/2025 120
24.2%
06/07/2025 88
17.8%
06/10/2025 86
17.4%
06/09/2025 77
15.6%
07/06/2025 53
10.7%
09/06/2025 18
 
3.6%
10/06/2025 18
 
3.6%
06/08/2025 13
 
2.6%
11/06/2025 10
 
2.0%
08/06/2025 6
 
1.2%
Other values (2) 6
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1469
29.7%
/ 989
20.0%
2 989
20.0%
6 615
12.4%
5 495
 
10.0%
7 142
 
2.9%
1 134
 
2.7%
9 95
 
1.9%
8 19
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1469
29.7%
/ 989
20.0%
2 989
20.0%
6 615
12.4%
5 495
 
10.0%
7 142
 
2.9%
1 134
 
2.7%
9 95
 
1.9%
8 19
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1469
29.7%
/ 989
20.0%
2 989
20.0%
6 615
12.4%
5 495
 
10.0%
7 142
 
2.9%
1 134
 
2.7%
9 95
 
1.9%
8 19
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1469
29.7%
/ 989
20.0%
2 989
20.0%
6 615
12.4%
5 495
 
10.0%
7 142
 
2.9%
1 134
 
2.7%
9 95
 
1.9%
8 19
 
0.4%

GI - Interviewer
Categorical

High correlation  Missing 

Distinct25
Distinct (%)6.6%
Missing124
Missing (%)24.8%
Memory size4.0 KiB
John Rey Plaza
42 
Mary Josie Fontanillla
31 
Francis Jucar
27 
Jeanette Corpuz
26 
Mary Jane Castro
24 
Other values (20)
226 

Length

Max length23
Median length20
Mean length16.218085
Min length13

Characters and Unicode

Total characters6098
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st rowMary Josie Fontanillla
2nd rowMary Josie Fontanillla
3rd rowMary Josie Fontanillla
4th rowMary Josie Fontanillla
5th rowMary Josie Fontanillla

Common Values

ValueCountFrequency (%)
John Rey Plaza 42
 
8.4%
Mary Josie Fontanillla 31
 
6.2%
Francis Jucar 27
 
5.4%
Jeanette Corpuz 26
 
5.2%
Mary Jane Castro 24
 
4.8%
Johncy Nisperos 24
 
4.8%
Trisha Cacdac 23
 
4.6%
Ardee Agustin 23
 
4.6%
Arvee Jeric Agustin 22
 
4.4%
Julius Mark Apilado 17
 
3.4%
Other values (15) 117
23.4%
(Missing) 124
24.8%

Length

2025-07-05T17:36:02.066596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nisperos 72
 
7.5%
mary 55
 
5.8%
agustin 54
 
5.6%
plaza 42
 
4.4%
rey 42
 
4.4%
john 42
 
4.4%
ardee 32
 
3.3%
josie 31
 
3.2%
fontanillla 31
 
3.2%
francis 27
 
2.8%
Other values (28) 528
55.2%

Most occurring characters

ValueCountFrequency (%)
a 688
 
11.3%
590
 
9.7%
e 501
 
8.2%
r 431
 
7.1%
i 403
 
6.6%
s 367
 
6.0%
l 343
 
5.6%
o 333
 
5.5%
n 333
 
5.5%
J 224
 
3.7%
Other values (23) 1885
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 688
 
11.3%
590
 
9.7%
e 501
 
8.2%
r 431
 
7.1%
i 403
 
6.6%
s 367
 
6.0%
l 343
 
5.6%
o 333
 
5.5%
n 333
 
5.5%
J 224
 
3.7%
Other values (23) 1885
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 688
 
11.3%
590
 
9.7%
e 501
 
8.2%
r 431
 
7.1%
i 403
 
6.6%
s 367
 
6.0%
l 343
 
5.6%
o 333
 
5.5%
n 333
 
5.5%
J 224
 
3.7%
Other values (23) 1885
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 688
 
11.3%
590
 
9.7%
e 501
 
8.2%
r 431
 
7.1%
i 403
 
6.6%
s 367
 
6.0%
l 343
 
5.6%
o 333
 
5.5%
n 333
 
5.5%
J 224
 
3.7%
Other values (23) 1885
30.9%

GI - Remarks (Small, Medium, Large, Residential)
Categorical

High correlation  Missing 

Distinct11
Distinct (%)2.3%
Missing31
Missing (%)6.2%
Memory size4.0 KiB
Small
143 
Residential
140 
small
45 
SMALL
44 
Medium
41 
Other values (6)
56 

Length

Max length11
Median length5
Mean length6.9296375
Min length5

Characters and Unicode

Total characters3250
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowResidential
2nd rowResidential
3rd rowResidential
4th rowResidential
5th rowResidential

Common Values

ValueCountFrequency (%)
Small 143
28.6%
Residential 140
28.0%
small 45
 
9.0%
SMALL 44
 
8.8%
Medium 41
 
8.2%
Large 30
 
6.0%
MEDIUM 15
 
3.0%
LARGE 5
 
1.0%
Medium 4
 
0.8%
large 1
 
0.2%
(Missing) 31
 
6.2%

Length

2025-07-05T17:36:02.178210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
small 232
49.5%
residential 140
29.9%
medium 61
 
13.0%
large 36
 
7.7%

Most occurring characters

ValueCountFrequency (%)
l 517
15.9%
a 359
11.0%
e 357
11.0%
i 326
10.0%
m 235
 
7.2%
S 187
 
5.8%
d 186
 
5.7%
s 185
 
5.7%
R 145
 
4.5%
n 140
 
4.3%
Other values (13) 613
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 517
15.9%
a 359
11.0%
e 357
11.0%
i 326
10.0%
m 235
 
7.2%
S 187
 
5.8%
d 186
 
5.7%
s 185
 
5.7%
R 145
 
4.5%
n 140
 
4.3%
Other values (13) 613
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 517
15.9%
a 359
11.0%
e 357
11.0%
i 326
10.0%
m 235
 
7.2%
S 187
 
5.8%
d 186
 
5.7%
s 185
 
5.7%
R 145
 
4.5%
n 140
 
4.3%
Other values (13) 613
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 517
15.9%
a 359
11.0%
e 357
11.0%
i 326
10.0%
m 235
 
7.2%
S 187
 
5.8%
d 186
 
5.7%
s 185
 
5.7%
R 145
 
4.5%
n 140
 
4.3%
Other values (13) 613
18.9%
Distinct456
Distinct (%)99.6%
Missing42
Missing (%)8.4%
Memory size4.0 KiB
2025-07-05T17:36:02.503494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length57
Median length41
Mean length17.368996
Min length3

Characters and Unicode

Total characters7955
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique454 ?
Unique (%)99.1%

Sample

1st rowRoss Carino
2nd rowRam Garcia
3rd rowSharie Jane Abando
4th rowMalidale Muna
5th rowLovely Colet
ValueCountFrequency (%)
eatery 25
 
2.0%
store 21
 
1.7%
food 21
 
1.7%
laundry 19
 
1.6%
17
 
1.4%
house 16
 
1.3%
station 11
 
0.9%
canteen 10
 
0.8%
sari 10
 
0.8%
wash 9
 
0.7%
Other values (856) 1061
87.0%
2025-07-05T17:36:03.013340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
770
 
9.7%
a 495
 
6.2%
A 396
 
5.0%
E 352
 
4.4%
e 349
 
4.4%
n 296
 
3.7%
R 276
 
3.5%
S 262
 
3.3%
o 259
 
3.3%
i 242
 
3.0%
Other values (64) 4258
53.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
770
 
9.7%
a 495
 
6.2%
A 396
 
5.0%
E 352
 
4.4%
e 349
 
4.4%
n 296
 
3.7%
R 276
 
3.5%
S 262
 
3.3%
o 259
 
3.3%
i 242
 
3.0%
Other values (64) 4258
53.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
770
 
9.7%
a 495
 
6.2%
A 396
 
5.0%
E 352
 
4.4%
e 349
 
4.4%
n 296
 
3.7%
R 276
 
3.5%
S 262
 
3.3%
o 259
 
3.3%
i 242
 
3.0%
Other values (64) 4258
53.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
770
 
9.7%
a 495
 
6.2%
A 396
 
5.0%
E 352
 
4.4%
e 349
 
4.4%
n 296
 
3.7%
R 276
 
3.5%
S 262
 
3.3%
o 259
 
3.3%
i 242
 
3.0%
Other values (64) 4258
53.5%

GI - Business Location/Address
Categorical

High correlation  Missing 

Distinct32
Distinct (%)8.2%
Missing112
Missing (%)22.4%
Memory size4.0 KiB
Sevilla
77 
CARLATAN CSF LU
52 
Lingsat
49 
Poro
27 
Pagdaraoan
25 
Other values (27)
158 

Length

Max length42
Median length32
Mean length10.31701
Min length4

Characters and Unicode

Total characters4003
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)4.6%

Sample

1st rowZone 5 Lubrin Heights
2nd rowBrgy I
3rd rowGapuz Zigzag Road
4th row163 burgos street
5th rowBARANGAY IV , SFC L.U

Common Values

ValueCountFrequency (%)
Sevilla 77
15.4%
CARLATAN CSF LU 52
10.4%
Lingsat 49
9.8%
Poro 27
 
5.4%
Pagdaraoan 25
 
5.0%
Tanqui 25
 
5.0%
Madayegdeg 24
 
4.8%
BARANGAY IV 23
 
4.6%
CATBANGEN SFC, LU 22
 
4.4%
Parian 14
 
2.8%
Other values (22) 50
10.0%
(Missing) 112
22.4%

Length

2025-07-05T17:36:03.178315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lu 85
12.6%
sevilla 77
11.4%
csf 62
 
9.2%
carlatan 52
 
7.7%
lingsat 49
 
7.2%
barangay 34
 
5.0%
iv 33
 
4.9%
sfc 33
 
4.9%
catbangen 32
 
4.7%
poro 28
 
4.1%
Other values (38) 192
28.4%

Most occurring characters

ValueCountFrequency (%)
a 380
 
9.5%
A 337
 
8.4%
290
 
7.2%
L 198
 
4.9%
i 197
 
4.9%
S 188
 
4.7%
n 184
 
4.6%
C 180
 
4.5%
l 159
 
4.0%
e 155
 
3.9%
Other values (43) 1735
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 380
 
9.5%
A 337
 
8.4%
290
 
7.2%
L 198
 
4.9%
i 197
 
4.9%
S 188
 
4.7%
n 184
 
4.6%
C 180
 
4.5%
l 159
 
4.0%
e 155
 
3.9%
Other values (43) 1735
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 380
 
9.5%
A 337
 
8.4%
290
 
7.2%
L 198
 
4.9%
i 197
 
4.9%
S 188
 
4.7%
n 184
 
4.6%
C 180
 
4.5%
l 159
 
4.0%
e 155
 
3.9%
Other values (43) 1735
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 380
 
9.5%
A 337
 
8.4%
290
 
7.2%
L 198
 
4.9%
i 197
 
4.9%
S 188
 
4.7%
n 184
 
4.6%
C 180
 
4.5%
l 159
 
4.0%
e 155
 
3.9%
Other values (43) 1735
43.3%

GI - Type of Business
Categorical

High correlation  Missing 

Distinct28
Distinct (%)9.2%
Missing197
Missing (%)39.4%
Memory size4.0 KiB
Restaurant / Food Service
147 
Retail
30 
Laundromat
20 
Food Service
 
14
Hotel Accomodation
 
11
Other values (23)
81 

Length

Max length26
Median length25
Mean length18.072607
Min length3

Characters and Unicode

Total characters5476
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)3.3%

Sample

1st rowOffice / Corporate
2nd rowRetail
3rd rowRetail
4th rowLaundromat
5th rowRestaurant / Food Service

Common Values

ValueCountFrequency (%)
Restaurant / Food Service 147
29.4%
Retail 30
 
6.0%
Laundromat 20
 
4.0%
Food Service 14
 
2.8%
Hotel Accomodation 11
 
2.2%
College / University 10
 
2.0%
Sari-Sari Store 8
 
1.6%
Manufacturing / Production 8
 
1.6%
Salon 7
 
1.4%
Car Wash 7
 
1.4%
Other values (18) 41
 
8.2%
(Missing) 197
39.4%

Length

2025-07-05T17:36:03.349823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
170
20.3%
service 165
19.7%
food 161
19.2%
restaurant 148
17.6%
retail 30
 
3.6%
laundromat 20
 
2.4%
hotel 11
 
1.3%
accomodation 11
 
1.3%
college 10
 
1.2%
university 10
 
1.2%
Other values (27) 103
12.3%

Most occurring characters

ValueCountFrequency (%)
e 582
 
10.6%
539
 
9.8%
a 460
 
8.4%
o 452
 
8.3%
t 438
 
8.0%
r 413
 
7.5%
i 306
 
5.6%
n 249
 
4.5%
c 217
 
4.0%
S 206
 
3.8%
Other values (29) 1614
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 582
 
10.6%
539
 
9.8%
a 460
 
8.4%
o 452
 
8.3%
t 438
 
8.0%
r 413
 
7.5%
i 306
 
5.6%
n 249
 
4.5%
c 217
 
4.0%
S 206
 
3.8%
Other values (29) 1614
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 582
 
10.6%
539
 
9.8%
a 460
 
8.4%
o 452
 
8.3%
t 438
 
8.0%
r 413
 
7.5%
i 306
 
5.6%
n 249
 
4.5%
c 217
 
4.0%
S 206
 
3.8%
Other values (29) 1614
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 582
 
10.6%
539
 
9.8%
a 460
 
8.4%
o 452
 
8.3%
t 438
 
8.0%
r 413
 
7.5%
i 306
 
5.6%
n 249
 
4.5%
c 217
 
4.0%
S 206
 
3.8%
Other values (29) 1614
29.5%

GI - No. of Employees
Text

Missing 

Distinct52
Distinct (%)20.9%
Missing251
Missing (%)50.2%
Memory size4.0 KiB
2025-07-05T17:36:04.054513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length1
Mean length3.0401606
Min length1

Characters and Unicode

Total characters757
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)12.0%

Sample

1st row7
2nd row3
3rd row5
4th row10
5th row15
ValueCountFrequency (%)
2 40
13.2%
3 35
 
11.6%
6 27
 
8.9%
5 25
 
8.3%
4 24
 
7.9%
1 13
 
4.3%
7 12
 
4.0%
empolyees 11
 
3.6%
customer 9
 
3.0%
30 8
 
2.6%
Other values (45) 98
32.5%
2025-07-05T17:36:05.780017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 67
 
8.9%
E 62
 
8.2%
0 60
 
7.9%
53
 
7.0%
3 52
 
6.9%
1 48
 
6.3%
5 42
 
5.5%
4 36
 
4.8%
S 34
 
4.5%
6 32
 
4.2%
Other values (18) 271
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 67
 
8.9%
E 62
 
8.2%
0 60
 
7.9%
53
 
7.0%
3 52
 
6.9%
1 48
 
6.3%
5 42
 
5.5%
4 36
 
4.8%
S 34
 
4.5%
6 32
 
4.2%
Other values (18) 271
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 67
 
8.9%
E 62
 
8.2%
0 60
 
7.9%
53
 
7.0%
3 52
 
6.9%
1 48
 
6.3%
5 42
 
5.5%
4 36
 
4.8%
S 34
 
4.5%
6 32
 
4.2%
Other values (18) 271
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 67
 
8.9%
E 62
 
8.2%
0 60
 
7.9%
53
 
7.0%
3 52
 
6.9%
1 48
 
6.3%
5 42
 
5.5%
4 36
 
4.8%
S 34
 
4.5%
6 32
 
4.2%
Other values (18) 271
35.8%

GI - Operating Hours - Start
Categorical

High correlation  Imbalance  Missing 

Distinct14
Distinct (%)4.3%
Missing178
Missing (%)35.6%
Memory size4.0 KiB
8:00
210 
0:00
49 
7:00
 
15
6:00
 
12
9:00
 
10
Other values (9)
26 

Length

Max length5
Median length4
Mean length4.0341615
Min length4

Characters and Unicode

Total characters1299
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.2%

Sample

1st row8:00
2nd row8:00
3rd row8:00
4th row8:00
5th row8:00

Common Values

ValueCountFrequency (%)
8:00 210
42.0%
0:00 49
 
9.8%
7:00 15
 
3.0%
6:00 12
 
2.4%
9:00 10
 
2.0%
10:00 7
 
1.4%
5:00 7
 
1.4%
4:00 3
 
0.6%
18:00 3
 
0.6%
7:30 2
 
0.4%
Other values (4) 4
 
0.8%
(Missing) 178
35.6%

Length

2025-07-05T17:36:06.179842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8:00 210
65.2%
0:00 49
 
15.2%
7:00 15
 
4.7%
6:00 12
 
3.7%
9:00 10
 
3.1%
10:00 7
 
2.2%
5:00 7
 
2.2%
4:00 3
 
0.9%
18:00 3
 
0.9%
7:30 2
 
0.6%
Other values (4) 4
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 696
53.6%
: 322
24.8%
8 214
 
16.5%
7 17
 
1.3%
6 12
 
0.9%
1 12
 
0.9%
9 11
 
0.8%
5 7
 
0.5%
3 5
 
0.4%
4 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 696
53.6%
: 322
24.8%
8 214
 
16.5%
7 17
 
1.3%
6 12
 
0.9%
1 12
 
0.9%
9 11
 
0.8%
5 7
 
0.5%
3 5
 
0.4%
4 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 696
53.6%
: 322
24.8%
8 214
 
16.5%
7 17
 
1.3%
6 12
 
0.9%
1 12
 
0.9%
9 11
 
0.8%
5 7
 
0.5%
3 5
 
0.4%
4 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 696
53.6%
: 322
24.8%
8 214
 
16.5%
7 17
 
1.3%
6 12
 
0.9%
1 12
 
0.9%
9 11
 
0.8%
5 7
 
0.5%
3 5
 
0.4%
4 3
 
0.2%

GI - Operating Hours - End
Categorical

High correlation  Imbalance  Missing 

Distinct19
Distinct (%)5.9%
Missing178
Missing (%)35.6%
Memory size4.0 KiB
17:00
211 
23:59
41 
21:00
 
13
22:00
 
12
19:00
 
9
Other values (14)
36 

Length

Max length5
Median length5
Mean length4.9782609
Min length4

Characters and Unicode

Total characters1603
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)2.2%

Sample

1st row17:00
2nd row17:00
3rd row17:00
4th row17:00
5th row17:00

Common Values

ValueCountFrequency (%)
17:00 211
42.2%
23:59 41
 
8.2%
21:00 13
 
2.6%
22:00 12
 
2.4%
19:00 9
 
1.8%
20:00 9
 
1.8%
18:00 9
 
1.8%
23:00 3
 
0.6%
16:00 2
 
0.4%
15:00 2
 
0.4%
Other values (9) 11
 
2.2%
(Missing) 178
35.6%

Length

2025-07-05T17:36:06.627022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17:00 211
65.5%
23:59 41
 
12.7%
21:00 13
 
4.0%
22:00 12
 
3.7%
19:00 9
 
2.8%
20:00 9
 
2.8%
18:00 9
 
2.8%
23:00 3
 
0.9%
16:00 2
 
0.6%
15:00 2
 
0.6%
Other values (9) 11
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 570
35.6%
: 322
20.1%
1 249
15.5%
7 213
 
13.3%
2 92
 
5.7%
9 50
 
3.1%
3 49
 
3.1%
5 43
 
2.7%
8 10
 
0.6%
6 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 570
35.6%
: 322
20.1%
1 249
15.5%
7 213
 
13.3%
2 92
 
5.7%
9 50
 
3.1%
3 49
 
3.1%
5 43
 
2.7%
8 10
 
0.6%
6 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 570
35.6%
: 322
20.1%
1 249
15.5%
7 213
 
13.3%
2 92
 
5.7%
9 50
 
3.1%
3 49
 
3.1%
5 43
 
2.7%
8 10
 
0.6%
6 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 570
35.6%
: 322
20.1%
1 249
15.5%
7 213
 
13.3%
2 92
 
5.7%
9 50
 
3.1%
3 49
 
3.1%
5 43
 
2.7%
8 10
 
0.6%
6 4
 
0.2%
Distinct2
Distinct (%)100.0%
Missing498
Missing (%)99.6%
Memory size4.0 KiB
2025-07-05T17:36:07.129901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length9
Min length7

Characters and Unicode

Total characters18
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowWashing
2nd rowFor Laundry
ValueCountFrequency (%)
washing 1
33.3%
for 1
33.3%
laundry 1
33.3%
2025-07-05T17:36:07.624171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
 
11.1%
n 2
 
11.1%
r 2
 
11.1%
s 1
 
5.6%
h 1
 
5.6%
i 1
 
5.6%
W 1
 
5.6%
g 1
 
5.6%
F 1
 
5.6%
o 1
 
5.6%
Other values (5) 5
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
 
11.1%
n 2
 
11.1%
r 2
 
11.1%
s 1
 
5.6%
h 1
 
5.6%
i 1
 
5.6%
W 1
 
5.6%
g 1
 
5.6%
F 1
 
5.6%
o 1
 
5.6%
Other values (5) 5
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
 
11.1%
n 2
 
11.1%
r 2
 
11.1%
s 1
 
5.6%
h 1
 
5.6%
i 1
 
5.6%
W 1
 
5.6%
g 1
 
5.6%
F 1
 
5.6%
o 1
 
5.6%
Other values (5) 5
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
 
11.1%
n 2
 
11.1%
r 2
 
11.1%
s 1
 
5.6%
h 1
 
5.6%
i 1
 
5.6%
W 1
 
5.6%
g 1
 
5.6%
F 1
 
5.6%
o 1
 
5.6%
Other values (5) 5
27.8%

GI - Members
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)8.1%
Missing389
Missing (%)77.8%
Infinite0
Infinite (%)0.0%
Mean4.6126126
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-05T17:36:07.789617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q35
95-th percentile9
Maximum10
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8048982
Coefficient of variation (CV)0.3912963
Kurtosis0.6964742
Mean4.6126126
Median Absolute Deviation (MAD)1
Skewness0.9760469
Sum512
Variance3.2576577
MonotonicityNot monotonic
2025-07-05T17:36:07.945149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 30
 
6.0%
3 24
 
4.8%
5 22
 
4.4%
6 13
 
2.6%
2 8
 
1.6%
9 6
 
1.2%
7 4
 
0.8%
8 3
 
0.6%
10 1
 
0.2%
(Missing) 389
77.8%
ValueCountFrequency (%)
2 8
 
1.6%
3 24
4.8%
4 30
6.0%
5 22
4.4%
6 13
2.6%
7 4
 
0.8%
8 3
 
0.6%
9 6
 
1.2%
10 1
 
0.2%
ValueCountFrequency (%)
10 1
 
0.2%
9 6
 
1.2%
8 3
 
0.6%
7 4
 
0.8%
6 13
2.6%
5 22
4.4%
4 30
6.0%
3 24
4.8%
2 8
 
1.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
346 
0
154 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 346
69.2%
0 154
30.8%

Length

2025-07-05T17:36:08.110520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:08.214059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 346
69.2%
0 154
30.8%

Most occurring characters

ValueCountFrequency (%)
1 346
69.2%
0 154
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 346
69.2%
0 154
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 346
69.2%
0 154
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 346
69.2%
0 154
30.8%

CWSU-1 - Usage - Food preparation
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
334 
0
166 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 334
66.8%
0 166
33.2%

Length

2025-07-05T17:36:08.371576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:08.494771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 334
66.8%
0 166
33.2%

Most occurring characters

ValueCountFrequency (%)
1 334
66.8%
0 166
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 334
66.8%
0 166
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 334
66.8%
0 166
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 334
66.8%
0 166
33.2%

CWSU-1 - Usage - Cleaning & Sanitation
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
388 
0
112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 388
77.6%
0 112
 
22.4%

Length

2025-07-05T17:36:08.674994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:08.785443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 388
77.6%
0 112
 
22.4%

Most occurring characters

ValueCountFrequency (%)
1 388
77.6%
0 112
 
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 388
77.6%
0 112
 
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 388
77.6%
0 112
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 388
77.6%
0 112
 
22.4%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
444 
1
56 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 444
88.8%
1 56
 
11.2%

Length

2025-07-05T17:36:08.953539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:09.077677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 444
88.8%
1 56
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 444
88.8%
1 56
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 444
88.8%
1 56
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 444
88.8%
1 56
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 444
88.8%
1 56
 
11.2%

CWSU-1 - Usage - Landscaping/Irrigation
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size4.0 KiB
0.0
478 
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1497
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 478
95.6%
1.0 21
 
4.2%
(Missing) 1
 
0.2%

Length

2025-07-05T17:36:09.234591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:09.381509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 478
95.8%
1.0 21
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 977
65.3%
. 499
33.3%
1 21
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 977
65.3%
. 499
33.3%
1 21
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 977
65.3%
. 499
33.3%
1 21
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 977
65.3%
. 499
33.3%
1 21
 
1.4%

CWSU-1 - Usage - Others
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
497 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Length

2025-07-05T17:36:09.567531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:09.678821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

CWSU-2 - Treatment
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
296 
1
204 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 296
59.2%
1 204
40.8%

Length

2025-07-05T17:36:09.802850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:09.915010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 296
59.2%
1 204
40.8%

Most occurring characters

ValueCountFrequency (%)
0 296
59.2%
1 204
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 296
59.2%
1 204
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 296
59.2%
1 204
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 296
59.2%
1 204
40.8%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
383 
1
117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

Length

2025-07-05T17:36:10.339271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:10.680285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

Most occurring characters

ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

CWSU-3 - Current Sources - Deep Well (owned)
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
339 
0
161 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 339
67.8%
0 161
32.2%

Length

2025-07-05T17:36:10.932198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:11.156846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 339
67.8%
0 161
32.2%

Most occurring characters

ValueCountFrequency (%)
1 339
67.8%
0 161
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 339
67.8%
0 161
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 339
67.8%
0 161
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 339
67.8%
0 161
32.2%
Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size4.0 KiB
0.0
439 
1.0
60 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1497
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 439
87.8%
1.0 60
 
12.0%
(Missing) 1
 
0.2%

Length

2025-07-05T17:36:11.417741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:11.675827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 439
88.0%
1.0 60
 
12.0%

Most occurring characters

ValueCountFrequency (%)
0 938
62.7%
. 499
33.3%
1 60
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 938
62.7%
. 499
33.3%
1 60
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 938
62.7%
. 499
33.3%
1 60
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 938
62.7%
. 499
33.3%
1 60
 
4.0%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
251 
0
249 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 251
50.2%
0 249
49.8%

Length

2025-07-05T17:36:11.969828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:12.155262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 251
50.2%
0 249
49.8%

Most occurring characters

ValueCountFrequency (%)
1 251
50.2%
0 249
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 251
50.2%
0 249
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 251
50.2%
0 249
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 251
50.2%
0 249
49.8%

CWSU-3 - Current Sources - Others
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size4.0 KiB
0.0
492 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1497
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 492
98.4%
1.0 7
 
1.4%
(Missing) 1
 
0.2%

Length

2025-07-05T17:36:12.412142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:12.598351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 492
98.6%
1.0 7
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 991
66.2%
. 499
33.3%
1 7
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 991
66.2%
. 499
33.3%
1 7
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 991
66.2%
. 499
33.3%
1 7
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 991
66.2%
. 499
33.3%
1 7
 
0.5%
Distinct9
Distinct (%)100.0%
Missing491
Missing (%)98.2%
Memory size4.0 KiB
2025-07-05T17:36:13.038742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11
Min length7

Characters and Unicode

Total characters99
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)100.0%

Sample

1st rowPump Well
2nd rowSharing
3rd rowElectric
4th rowpurified water
5th rowELECTRIC WATER PUMP
ValueCountFrequency (%)
water 3
18.8%
electric 2
12.5%
pump 2
12.5%
mall 2
12.5%
well 1
 
6.2%
sharing 1
 
6.2%
purified 1
 
6.2%
manila 1
 
6.2%
6gals/day 1
 
6.2%
dept 1
 
6.2%
2025-07-05T17:36:13.969983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 10
 
10.1%
a 9
 
9.1%
7
 
7.1%
e 6
 
6.1%
r 5
 
5.1%
i 5
 
5.1%
p 5
 
5.1%
M 4
 
4.0%
t 4
 
4.0%
E 4
 
4.0%
Other values (24) 40
40.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 10
 
10.1%
a 9
 
9.1%
7
 
7.1%
e 6
 
6.1%
r 5
 
5.1%
i 5
 
5.1%
p 5
 
5.1%
M 4
 
4.0%
t 4
 
4.0%
E 4
 
4.0%
Other values (24) 40
40.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 10
 
10.1%
a 9
 
9.1%
7
 
7.1%
e 6
 
6.1%
r 5
 
5.1%
i 5
 
5.1%
p 5
 
5.1%
M 4
 
4.0%
t 4
 
4.0%
E 4
 
4.0%
Other values (24) 40
40.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 10
 
10.1%
a 9
 
9.1%
7
 
7.1%
e 6
 
6.1%
r 5
 
5.1%
i 5
 
5.1%
p 5
 
5.1%
M 4
 
4.0%
t 4
 
4.0%
E 4
 
4.0%
Other values (24) 40
40.4%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
461 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 461
92.2%
1 39
 
7.8%

Length

2025-07-05T17:36:14.342615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:14.494313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 461
92.2%
1 39
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 461
92.2%
1 39
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 461
92.2%
1 39
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 461
92.2%
1 39
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 461
92.2%
1 39
 
7.8%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
436 
1
64 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Length

2025-07-05T17:36:14.591467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:14.671510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Most occurring characters

ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 436
87.2%
1 64
 
12.8%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
472 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 472
94.4%
1 28
 
5.6%

Length

2025-07-05T17:36:14.772945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:14.858745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 472
94.4%
1 28
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 472
94.4%
1 28
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 472
94.4%
1 28
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 472
94.4%
1 28
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 472
94.4%
1 28
 
5.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
377 
0
123 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 377
75.4%
0 123
 
24.6%

Length

2025-07-05T17:36:14.967405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:15.057766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 377
75.4%
0 123
 
24.6%

Most occurring characters

ValueCountFrequency (%)
1 377
75.4%
0 123
 
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 377
75.4%
0 123
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 377
75.4%
0 123
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 377
75.4%
0 123
 
24.6%

CWSU-4 - Primary Drinking Source - Others
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size4.0 KiB
0.0
486 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1497
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 486
97.2%
1.0 13
 
2.6%
(Missing) 1
 
0.2%

Length

2025-07-05T17:36:15.155117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:15.224750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 486
97.4%
1.0 13
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 985
65.8%
. 499
33.3%
1 13
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 985
65.8%
. 499
33.3%
1 13
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 985
65.8%
. 499
33.3%
1 13
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 985
65.8%
. 499
33.3%
1 13
 
0.9%
Distinct11
Distinct (%)78.6%
Missing486
Missing (%)97.2%
Memory size4.0 KiB
2025-07-05T17:36:15.369253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length14.5
Mean length10.785714
Min length3

Characters and Unicode

Total characters151
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)57.1%

Sample

1st rowMINERAL WATERS
2nd rowfiltered water
3rd rowown
4th rowown
5th rowPURIFIED
ValueCountFrequency (%)
purified 3
13.6%
station 3
13.6%
refilling 3
13.6%
water 3
13.6%
own 2
9.1%
filtered 1
 
4.5%
mineral 1
 
4.5%
waters 1
 
4.5%
refelling 1
 
4.5%
owned 1
 
4.5%
Other values (3) 3
13.6%
2025-07-05T17:36:15.669320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 11
 
7.3%
T 8
 
5.3%
e 8
 
5.3%
E 8
 
5.3%
8
 
5.3%
N 7
 
4.6%
L 7
 
4.6%
R 7
 
4.6%
i 7
 
4.6%
l 7
 
4.6%
Other values (23) 73
48.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 11
 
7.3%
T 8
 
5.3%
e 8
 
5.3%
E 8
 
5.3%
8
 
5.3%
N 7
 
4.6%
L 7
 
4.6%
R 7
 
4.6%
i 7
 
4.6%
l 7
 
4.6%
Other values (23) 73
48.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 11
 
7.3%
T 8
 
5.3%
e 8
 
5.3%
E 8
 
5.3%
8
 
5.3%
N 7
 
4.6%
L 7
 
4.6%
R 7
 
4.6%
i 7
 
4.6%
l 7
 
4.6%
Other values (23) 73
48.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 11
 
7.3%
T 8
 
5.3%
e 8
 
5.3%
E 8
 
5.3%
8
 
5.3%
N 7
 
4.6%
L 7
 
4.6%
R 7
 
4.6%
i 7
 
4.6%
l 7
 
4.6%
Other values (23) 73
48.3%

CWSU-4 - Primary Drinking Source - gals
Real number (ℝ)

High correlation 

Distinct80
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.56062
Minimum6.63
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-05T17:36:15.792654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.63
5-th percentile6.63
Q120
median50
Q3125
95-th percentile365
Maximum365
Range358.37
Interquartile range (IQR)105

Descriptive statistics

Standard deviation99.379161
Coefficient of variation (CV)1.085392
Kurtosis1.5481331
Mean91.56062
Median Absolute Deviation (MAD)34.7
Skewness1.5599292
Sum45780.31
Variance9876.2177
MonotonicityNot monotonic
2025-07-05T17:36:15.970668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 44
 
8.8%
365 32
 
6.4%
6.63 29
 
5.8%
204.21 22
 
4.4%
60 20
 
4.0%
150 20
 
4.0%
25 19
 
3.8%
12 17
 
3.4%
30 17
 
3.4%
40 14
 
2.8%
Other values (70) 266
53.2%
ValueCountFrequency (%)
6.63 29
5.8%
7 1
 
0.2%
8 6
 
1.2%
9 1
 
0.2%
10 11
 
2.2%
12 17
3.4%
12.7 1
 
0.2%
15 8
 
1.6%
15.3 5
 
1.0%
16 6
 
1.2%
ValueCountFrequency (%)
365 32
6.4%
350 1
 
0.2%
317 1
 
0.2%
300 9
 
1.8%
275 1
 
0.2%
250 2
 
0.4%
240 1
 
0.2%
225 1
 
0.2%
220 2
 
0.4%
204.21 22
4.4%

CWSU-5 - Ave Demand (in cbm)
Real number (ℝ)

High correlation  Missing 

Distinct124
Distinct (%)57.9%
Missing286
Missing (%)57.2%
Infinite0
Infinite (%)0.0%
Mean75.902411
Minimum0.03785
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-05T17:36:16.154304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.03785
5-th percentile0.44555225
Q12.017
median5
Q328.290625
95-th percentile282.45
Maximum2500
Range2499.9621
Interquartile range (IQR)26.273625

Descriptive statistics

Standard deviation268.85394
Coefficient of variation (CV)3.5421001
Kurtosis46.701858
Mean75.902411
Median Absolute Deviation (MAD)4.3187
Skewness6.3879604
Sum16243.116
Variance72282.439
MonotonicityNot monotonic
2025-07-05T17:36:16.922574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 6
 
1.2%
3 6
 
1.2%
3.4065 5
 
1.0%
4 5
 
1.0%
2.83875 5
 
1.0%
2.271 4
 
0.8%
5 4
 
0.8%
1.70325 4
 
0.8%
20 4
 
0.8%
1.1355 4
 
0.8%
Other values (114) 167
33.4%
(Missing) 286
57.2%
ValueCountFrequency (%)
0.03785 2
0.4%
0.0757 2
0.4%
0.1 1
0.2%
0.11355 1
0.2%
0.2 1
0.2%
0.3 2
0.4%
0.34065 1
0.2%
0.344435 1
0.2%
0.5 2
0.4%
0.510975 1
0.2%
ValueCountFrequency (%)
2500 1
0.2%
2000 1
0.2%
1500 1
0.2%
900 2
0.4%
840 1
0.2%
625 1
0.2%
600 1
0.2%
480 1
0.2%
360 1
0.2%
300 1
0.2%

CWSU-6 - Actual Use (m3 / gal) - Tap Water (Water District etc.)
Real number (ℝ)

High correlation  Missing 

Distinct15
Distinct (%)35.7%
Missing458
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean417.9028
Minimum180
Maximum2642.0079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-05T17:36:17.085163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile180
Q1180
median180
Q3337.5
95-th percentile1571.9947
Maximum2642.0079
Range2462.0079
Interquartile range (IQR)157.5

Descriptive statistics

Standard deviation530.95544
Coefficient of variation (CV)1.2705238
Kurtosis7.9569105
Mean417.9028
Median Absolute Deviation (MAD)0
Skewness2.7804324
Sum17551.918
Variance281913.68
MonotonicityNot monotonic
2025-07-05T17:36:17.218010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
180 27
 
5.4%
200 2
 
0.4%
400 1
 
0.2%
300 1
 
0.2%
500 1
 
0.2%
819.89 1
 
0.2%
350 1
 
0.2%
2642.007926 1
 
0.2%
1585.204756 1
 
0.2%
258 1
 
0.2%
Other values (5) 5
 
1.0%
(Missing) 458
91.6%
ValueCountFrequency (%)
180 27
5.4%
200 2
 
0.4%
258 1
 
0.2%
300 1
 
0.2%
350 1
 
0.2%
400 1
 
0.2%
417 1
 
0.2%
500 1
 
0.2%
792.6023778 1
 
0.2%
819.89 1
 
0.2%
ValueCountFrequency (%)
2642.007926 1
0.2%
1849.405548 1
0.2%
1585.204756 1
0.2%
1321.003963 1
0.2%
1056.80317 1
0.2%
819.89 1
0.2%
792.6023778 1
0.2%
500 1
0.2%
417 1
0.2%
400 1
0.2%

CWSU-6 - Actual Use (m3 / gal) - Deep Well (owned)
Real number (ℝ)

High correlation  Missing 

Distinct76
Distinct (%)52.1%
Missing354
Missing (%)70.8%
Infinite0
Infinite (%)0.0%
Mean4947.3153
Minimum8.51715
Maximum65521.797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-07-05T17:36:17.369462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.51715
5-th percentile8.51715
Q120
median300
Q33450
95-th percentile25759.577
Maximum65521.797
Range65513.279
Interquartile range (IQR)3430

Descriptive statistics

Standard deviation11576.857
Coefficient of variation (CV)2.3400281
Kurtosis11.75761
Mean4947.3153
Median Absolute Deviation (MAD)291.48285
Skewness3.356619
Sum722308.03
Variance1.3402361 × 108
MonotonicityNot monotonic
2025-07-05T17:36:17.534938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.51715 26
 
5.2%
300 9
 
1.8%
750 5
 
1.0%
20 5
 
1.0%
5284.015852 5
 
1.0%
1321.003963 4
 
0.8%
7926.023778 4
 
0.8%
23778.07133 3
 
0.6%
10 3
 
0.6%
40 3
 
0.6%
Other values (66) 79
 
15.8%
(Missing) 354
70.8%
ValueCountFrequency (%)
8.51715 26
5.2%
9.4635 1
 
0.2%
9.6 1
 
0.2%
10 3
 
0.6%
11.3562 1
 
0.2%
12.5 2
 
0.4%
20 5
 
1.0%
22.224 1
 
0.2%
23 1
 
0.2%
24 1
 
0.2%
ValueCountFrequency (%)
65521.79657 1
 
0.2%
57331.57199 1
 
0.2%
52840.15852 2
0.4%
46235.13871 1
 
0.2%
39630.11889 1
 
0.2%
29062.08719 1
 
0.2%
26420.07926 1
 
0.2%
23778.07133 3
0.6%
22457.06737 1
 
0.2%
18494.05548 1
 
0.2%

CWSU-6 - Actual Use (m3 / gal) - Truck Delivery Services (5 m3)
Unsupported

Missing  Rejected  Unsupported 

Missing464
Missing (%)92.8%
Memory size4.0 KiB

CWSU-6 - Actual Use (m3 / gal) - Bottled water (5 gallons)
Unsupported

Missing  Rejected  Unsupported 

Missing222
Missing (%)44.4%
Memory size4.0 KiB
Distinct8
Distinct (%)66.7%
Missing488
Missing (%)97.6%
Memory size4.0 KiB
2025-07-05T17:36:18.057604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.1666667
Min length1

Characters and Unicode

Total characters50
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)50.0%

Sample

1st rowSharing
2nd rowSharing
3rd rowSharing
4th rowSharing
5th row270
ValueCountFrequency (%)
sharing 4
33.3%
1 2
16.7%
180 1
 
8.3%
270 1
 
8.3%
300 1
 
8.3%
7,200 1
 
8.3%
450 1
 
8.3%
200 1
 
8.3%
2025-07-05T17:36:19.484165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9
18.0%
S 4
8.0%
a 4
8.0%
h 4
8.0%
i 4
8.0%
n 4
8.0%
g 4
8.0%
r 4
8.0%
1 3
 
6.0%
2 3
 
6.0%
Other values (6) 7
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9
18.0%
S 4
8.0%
a 4
8.0%
h 4
8.0%
i 4
8.0%
n 4
8.0%
g 4
8.0%
r 4
8.0%
1 3
 
6.0%
2 3
 
6.0%
Other values (6) 7
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9
18.0%
S 4
8.0%
a 4
8.0%
h 4
8.0%
i 4
8.0%
n 4
8.0%
g 4
8.0%
r 4
8.0%
1 3
 
6.0%
2 3
 
6.0%
Other values (6) 7
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9
18.0%
S 4
8.0%
a 4
8.0%
h 4
8.0%
i 4
8.0%
n 4
8.0%
g 4
8.0%
r 4
8.0%
1 3
 
6.0%
2 3
 
6.0%
Other values (6) 7
14.0%

CWSU-7 - Peak Month - Start
Categorical

High correlation  Missing 

Distinct14
Distinct (%)4.0%
Missing151
Missing (%)30.2%
Memory size4.0 KiB
March
178 
April
64 
December
27 
May
22 
January
 
15
Other values (9)
43 

Length

Max length9
Median length5
Mean length5.3352436
Min length3

Characters and Unicode

Total characters1862
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st rowFeb
2nd rowMarch
3rd rowMarch
4th rowMarch
5th rowMarch

Common Values

ValueCountFrequency (%)
March 178
35.6%
April 64
 
12.8%
December 27
 
5.4%
May 22
 
4.4%
January 15
 
3.0%
February 13
 
2.6%
June 10
 
2.0%
October 8
 
1.6%
August 4
 
0.8%
Feb 3
 
0.6%
Other values (4) 5
 
1.0%
(Missing) 151
30.2%

Length

2025-07-05T17:36:19.622007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
march 178
51.0%
april 64
 
18.3%
december 27
 
7.7%
may 22
 
6.3%
january 15
 
4.3%
february 13
 
3.7%
june 10
 
2.9%
october 8
 
2.3%
august 4
 
1.1%
feb 3
 
0.9%
Other values (4) 5
 
1.4%

Most occurring characters

ValueCountFrequency (%)
r 321
17.2%
a 244
13.1%
c 213
11.4%
M 200
10.7%
h 178
9.6%
e 122
 
6.6%
A 68
 
3.7%
p 65
 
3.5%
l 65
 
3.5%
i 64
 
3.4%
Other values (16) 322
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 321
17.2%
a 244
13.1%
c 213
11.4%
M 200
10.7%
h 178
9.6%
e 122
 
6.6%
A 68
 
3.7%
p 65
 
3.5%
l 65
 
3.5%
i 64
 
3.4%
Other values (16) 322
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 321
17.2%
a 244
13.1%
c 213
11.4%
M 200
10.7%
h 178
9.6%
e 122
 
6.6%
A 68
 
3.7%
p 65
 
3.5%
l 65
 
3.5%
i 64
 
3.4%
Other values (16) 322
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 321
17.2%
a 244
13.1%
c 213
11.4%
M 200
10.7%
h 178
9.6%
e 122
 
6.6%
A 68
 
3.7%
p 65
 
3.5%
l 65
 
3.5%
i 64
 
3.4%
Other values (16) 322
17.3%

CWSU-7 - Peak Month - End
Categorical

High correlation  Missing 

Distinct13
Distinct (%)3.7%
Missing151
Missing (%)30.2%
Memory size4.0 KiB
May
171 
April
57 
June
39 
December
34 
March
24 
Other values (8)
24 

Length

Max length9
Median length8
Mean length4.2922636
Min length3

Characters and Unicode

Total characters1498
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st rowJune
2nd rowJune
3rd rowJune
4th rowJune
5th rowJune

Common Values

ValueCountFrequency (%)
May 171
34.2%
April 57
 
11.4%
June 39
 
7.8%
December 34
 
6.8%
March 24
 
4.8%
August 7
 
1.4%
January 6
 
1.2%
July 4
 
0.8%
November 2
 
0.4%
September 2
 
0.4%
Other values (3) 3
 
0.6%
(Missing) 151
30.2%

Length

2025-07-05T17:36:19.753591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 171
49.0%
april 57
 
16.3%
june 39
 
11.2%
december 34
 
9.7%
march 24
 
6.9%
august 7
 
2.0%
january 6
 
1.7%
july 4
 
1.1%
november 2
 
0.6%
september 2
 
0.6%
Other values (3) 3
 
0.9%

Most occurring characters

ValueCountFrequency (%)
a 208
13.9%
M 195
13.0%
y 182
12.1%
e 153
10.2%
r 127
 
8.5%
u 65
 
4.3%
A 64
 
4.3%
l 61
 
4.1%
p 59
 
3.9%
i 58
 
3.9%
Other values (19) 326
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 208
13.9%
M 195
13.0%
y 182
12.1%
e 153
10.2%
r 127
 
8.5%
u 65
 
4.3%
A 64
 
4.3%
l 61
 
4.1%
p 59
 
3.9%
i 58
 
3.9%
Other values (19) 326
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 208
13.9%
M 195
13.0%
y 182
12.1%
e 153
10.2%
r 127
 
8.5%
u 65
 
4.3%
A 64
 
4.3%
l 61
 
4.1%
p 59
 
3.9%
i 58
 
3.9%
Other values (19) 326
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 208
13.9%
M 195
13.0%
y 182
12.1%
e 153
10.2%
r 127
 
8.5%
u 65
 
4.3%
A 64
 
4.3%
l 61
 
4.1%
p 59
 
3.9%
i 58
 
3.9%
Other values (19) 326
21.8%

CWSU-8 - Monthly Costs - Less than PhP 1,000
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
284 
1
216 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 284
56.8%
1 216
43.2%

Length

2025-07-05T17:36:19.871923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:19.942044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 284
56.8%
1 216
43.2%

Most occurring characters

ValueCountFrequency (%)
0 284
56.8%
1 216
43.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 284
56.8%
1 216
43.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 284
56.8%
1 216
43.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 284
56.8%
1 216
43.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
399 
1
101 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

Length

2025-07-05T17:36:20.037975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:20.164754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

Most occurring characters

ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

CWSU-8 - Monthly Costs - PhP 3,000 - PhP 5,000
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
465 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 465
93.0%
1 35
 
7.0%

Length

2025-07-05T17:36:20.348602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:20.472865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 465
93.0%
1 35
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 465
93.0%
1 35
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 465
93.0%
1 35
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 465
93.0%
1 35
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 465
93.0%
1 35
 
7.0%

CWSU-8 - Monthly Costs - PhP 5,000 - PhP 10,000
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
481 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Length

2025-07-05T17:36:20.609479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:20.711857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

CWSU-8 - Monthly Costs - PhP 10,000 and above
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
478 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%

Length

2025-07-05T17:36:20.846586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:20.968326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%
Distinct53
Distinct (%)42.7%
Missing376
Missing (%)75.2%
Memory size4.0 KiB
2025-07-05T17:36:21.287500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3870968
Min length2

Characters and Unicode

Total characters420
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)25.8%

Sample

1st row600
2nd row180
3rd row360
4th row120
5th row360
ValueCountFrequency (%)
600 14
 
11.3%
360 9
 
7.3%
240 6
 
4.8%
450 6
 
4.8%
750 6
 
4.8%
900 6
 
4.8%
120 5
 
4.0%
700 4
 
3.2%
480 4
 
3.2%
300 4
 
3.2%
Other values (43) 60
48.4%
2025-07-05T17:36:22.164326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 182
43.3%
5 37
 
8.8%
2 36
 
8.6%
1 33
 
7.9%
6 30
 
7.1%
4 26
 
6.2%
3 19
 
4.5%
7 18
 
4.3%
, 15
 
3.6%
8 13
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 182
43.3%
5 37
 
8.8%
2 36
 
8.6%
1 33
 
7.9%
6 30
 
7.1%
4 26
 
6.2%
3 19
 
4.5%
7 18
 
4.3%
, 15
 
3.6%
8 13
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 182
43.3%
5 37
 
8.8%
2 36
 
8.6%
1 33
 
7.9%
6 30
 
7.1%
4 26
 
6.2%
3 19
 
4.5%
7 18
 
4.3%
, 15
 
3.6%
8 13
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 182
43.3%
5 37
 
8.8%
2 36
 
8.6%
1 33
 
7.9%
6 30
 
7.1%
4 26
 
6.2%
3 19
 
4.5%
7 18
 
4.3%
, 15
 
3.6%
8 13
 
3.1%

CWSU-8 - Monthly Costs - Truck Delivery
Categorical

High correlation  Imbalance  Missing 

Distinct12
Distinct (%)16.0%
Missing425
Missing (%)85.0%
Memory size4.0 KiB
FALSE
63 
3000
 
2
2800
 
1
1400
 
1
300
 
1
Other values (7)

Length

Max length5
Median length5
Mean length4.7866667
Min length3

Characters and Unicode

Total characters359
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)13.3%

Sample

1st row2800
2nd row1400
3rd row400
4th row300
5th row600

Common Values

ValueCountFrequency (%)
FALSE 63
 
12.6%
3000 2
 
0.4%
2800 1
 
0.2%
1400 1
 
0.2%
300 1
 
0.2%
400 1
 
0.2%
5600 1
 
0.2%
600 1
 
0.2%
2600 1
 
0.2%
200 1
 
0.2%
Other values (2) 2
 
0.4%
(Missing) 425
85.0%

Length

2025-07-05T17:36:22.596743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
false 63
84.0%
3000 2
 
2.7%
2800 1
 
1.3%
1400 1
 
1.3%
300 1
 
1.3%
400 1
 
1.3%
5600 1
 
1.3%
600 1
 
1.3%
2600 1
 
1.3%
200 1
 
1.3%
Other values (2) 2
 
2.7%

Most occurring characters

ValueCountFrequency (%)
F 63
17.5%
A 63
17.5%
L 63
17.5%
S 63
17.5%
E 63
17.5%
0 26
7.2%
3 3
 
0.8%
2 3
 
0.8%
6 3
 
0.8%
5 3
 
0.8%
Other values (4) 6
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 63
17.5%
A 63
17.5%
L 63
17.5%
S 63
17.5%
E 63
17.5%
0 26
7.2%
3 3
 
0.8%
2 3
 
0.8%
6 3
 
0.8%
5 3
 
0.8%
Other values (4) 6
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 63
17.5%
A 63
17.5%
L 63
17.5%
S 63
17.5%
E 63
17.5%
0 26
7.2%
3 3
 
0.8%
2 3
 
0.8%
6 3
 
0.8%
5 3
 
0.8%
Other values (4) 6
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 63
17.5%
A 63
17.5%
L 63
17.5%
S 63
17.5%
E 63
17.5%
0 26
7.2%
3 3
 
0.8%
2 3
 
0.8%
6 3
 
0.8%
5 3
 
0.8%
Other values (4) 6
 
1.7%

CWSU-8 - Monthly Costs - Others:
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
487 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

Length

2025-07-05T17:36:22.929196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:23.144903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%
Distinct11
Distinct (%)91.7%
Missing488
Missing (%)97.6%
Memory size4.0 KiB
2025-07-05T17:36:23.560602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length9
Mean length5.6666667
Min length2

Characters and Unicode

Total characters68
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)83.3%

Sample

1st row480
2nd row1,800
3rd row35
4th row1,100
5th row300
ValueCountFrequency (%)
300 2
14.3%
1,800 1
 
7.1%
480 1
 
7.1%
35 1
 
7.1%
1,100 1
 
7.1%
deepwell 1
 
7.1%
motorized 1
 
7.1%
deep 1
 
7.1%
well 1
 
7.1%
2,000 1
 
7.1%
Other values (3) 3
21.4%
2025-07-05T17:36:24.487124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20
29.4%
e 7
 
10.3%
1 5
 
7.4%
, 4
 
5.9%
l 4
 
5.9%
2 3
 
4.4%
3 3
 
4.4%
8 2
 
2.9%
o 2
 
2.9%
2
 
2.9%
Other values (14) 16
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20
29.4%
e 7
 
10.3%
1 5
 
7.4%
, 4
 
5.9%
l 4
 
5.9%
2 3
 
4.4%
3 3
 
4.4%
8 2
 
2.9%
o 2
 
2.9%
2
 
2.9%
Other values (14) 16
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20
29.4%
e 7
 
10.3%
1 5
 
7.4%
, 4
 
5.9%
l 4
 
5.9%
2 3
 
4.4%
3 3
 
4.4%
8 2
 
2.9%
o 2
 
2.9%
2
 
2.9%
Other values (14) 16
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20
29.4%
e 7
 
10.3%
1 5
 
7.4%
, 4
 
5.9%
l 4
 
5.9%
2 3
 
4.4%
3 3
 
4.4%
8 2
 
2.9%
o 2
 
2.9%
2
 
2.9%
Other values (14) 16
23.5%

CWSU-9 - Issues (Tap Water) - Yes
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
414 
1
86 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

Length

2025-07-05T17:36:24.857933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:25.565446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
288 
1
212 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

Length

2025-07-05T17:36:25.660605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:25.735222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

Most occurring characters

ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

CWSU-9 - Issues (Tap Water) - N/A
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
372 
1
128 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 372
74.4%
1 128
 
25.6%

Length

2025-07-05T17:36:25.829644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:25.902351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 372
74.4%
1 128
 
25.6%

Most occurring characters

ValueCountFrequency (%)
0 372
74.4%
1 128
 
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 372
74.4%
1 128
 
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 372
74.4%
1 128
 
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 372
74.4%
1 128
 
25.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
441 
1
59 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 441
88.2%
1 59
 
11.8%

Length

2025-07-05T17:36:25.995271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:26.084447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 441
88.2%
1 59
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 441
88.2%
1 59
 
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 441
88.2%
1 59
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 441
88.2%
1 59
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 441
88.2%
1 59
 
11.8%

CWSU-9 - Issues (Tap Water) - Salinity
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
489 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

Length

2025-07-05T17:36:26.176446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:26.248037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

CWSU-9 - Issues (Tap Water) - Taste
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
487 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

Length

2025-07-05T17:36:26.343466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:26.412240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 487
97.4%
1 13
 
2.6%

CWSU-9 - Issues (Tap Water) - Smell
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
482 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 482
96.4%
1 18
 
3.6%

Length

2025-07-05T17:36:26.508216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:26.599208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 482
96.4%
1 18
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 482
96.4%
1 18
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 482
96.4%
1 18
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 482
96.4%
1 18
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 482
96.4%
1 18
 
3.6%

CWSU-9 - Issues (Tap Water) - Turbidity
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
495 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

Length

2025-07-05T17:36:26.678221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:26.750279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

CWSU-9 - Issues (Tap Water) - Costs
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size4.0 KiB

CWSU-9 - Issues (Tap Water) - Others
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
495 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

Length

2025-07-05T17:36:26.841961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:26.919258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 495
99.0%
1 5
 
1.0%
Distinct8
Distinct (%)100.0%
Missing492
Missing (%)98.4%
Memory size4.0 KiB
2025-07-05T17:36:27.258072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length12.5
Mean length12.5
Min length5

Characters and Unicode

Total characters100
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st rowNot enough water in well
2nd rowWalang gripo
3rd rowMAPUTIK
4th rowNAG YELLOW
5th rowwalang gripo
ValueCountFrequency (%)
gripo 2
 
11.1%
walang 2
 
11.1%
not 1
 
5.6%
enough 1
 
5.6%
in 1
 
5.6%
water 1
 
5.6%
well 1
 
5.6%
maputik 1
 
5.6%
nag 1
 
5.6%
yellow 1
 
5.6%
Other values (6) 6
33.3%
2025-07-05T17:36:29.204220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
 
10.0%
a 8
 
8.0%
g 7
 
7.0%
n 6
 
6.0%
e 5
 
5.0%
o 5
 
5.0%
i 5
 
5.0%
l 4
 
4.0%
t 4
 
4.0%
r 4
 
4.0%
Other values (25) 42
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10
 
10.0%
a 8
 
8.0%
g 7
 
7.0%
n 6
 
6.0%
e 5
 
5.0%
o 5
 
5.0%
i 5
 
5.0%
l 4
 
4.0%
t 4
 
4.0%
r 4
 
4.0%
Other values (25) 42
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10
 
10.0%
a 8
 
8.0%
g 7
 
7.0%
n 6
 
6.0%
e 5
 
5.0%
o 5
 
5.0%
i 5
 
5.0%
l 4
 
4.0%
t 4
 
4.0%
r 4
 
4.0%
Other values (25) 42
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10
 
10.0%
a 8
 
8.0%
g 7
 
7.0%
n 6
 
6.0%
e 5
 
5.0%
o 5
 
5.0%
i 5
 
5.0%
l 4
 
4.0%
t 4
 
4.0%
r 4
 
4.0%
Other values (25) 42
42.0%

CWSU-10 - Issues (Deep Well) - Yes
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
414 
1
86 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

Length

2025-07-05T17:36:29.957050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:30.053766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 414
82.8%
1 86
 
17.2%

CWSU-10 - Issues (Deep Well) - No
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
293 
0
207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 293
58.6%
0 207
41.4%

Length

2025-07-05T17:36:30.144445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:30.216175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 293
58.6%
0 207
41.4%

Most occurring characters

ValueCountFrequency (%)
1 293
58.6%
0 207
41.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 293
58.6%
0 207
41.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 293
58.6%
0 207
41.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 293
58.6%
0 207
41.4%

CWSU-10 - Issues (Deep Well) - N/A
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
433 
1
67 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 433
86.6%
1 67
 
13.4%

Length

2025-07-05T17:36:30.321619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:30.397725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 433
86.6%
1 67
 
13.4%

Most occurring characters

ValueCountFrequency (%)
0 433
86.6%
1 67
 
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 433
86.6%
1 67
 
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 433
86.6%
1 67
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 433
86.6%
1 67
 
13.4%

CWSU-10 - Issues (Deep Well) - Supply interruption
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
459 
1
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

Length

2025-07-05T17:36:30.491954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:30.565023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

CWSU-10 - Issues (Deep Well) - Salinity
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
489 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

Length

2025-07-05T17:36:30.659950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:30.739796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 489
97.8%
1 11
 
2.2%

CWSU-10 - Issues (Deep Well) - Taste
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
476 
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

Length

2025-07-05T17:36:30.841888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:30.915999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

CWSU-10 - Issues (Deep Well) - Smell
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
475 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 475
95.0%
1 25
 
5.0%

Length

2025-07-05T17:36:30.995052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:31.065337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 475
95.0%
1 25
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 475
95.0%
1 25
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 475
95.0%
1 25
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 475
95.0%
1 25
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 475
95.0%
1 25
 
5.0%

CWSU-10 - Issues (Deep Well) - Turbidity
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
483 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 483
96.6%
1 17
 
3.4%

Length

2025-07-05T17:36:31.158543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:31.230055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 483
96.6%
1 17
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 483
96.6%
1 17
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 483
96.6%
1 17
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 483
96.6%
1 17
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 483
96.6%
1 17
 
3.4%

CWSU-10 - Issues (Deep Well) - Costs
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
498 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Length

2025-07-05T17:36:31.327126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:31.399084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

CWSU-10 - Issues (Deep Well) - Others
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
484 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
96.8%
1 16
 
3.2%

Length

2025-07-05T17:36:31.487710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:31.565065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
96.8%
1 16
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 484
96.8%
1 16
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 484
96.8%
1 16
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 484
96.8%
1 16
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 484
96.8%
1 16
 
3.2%
Distinct16
Distinct (%)100.0%
Missing484
Missing (%)96.8%
Memory size4.0 KiB
2025-07-05T17:36:32.675469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length14
Mean length12.0625
Min length5

Characters and Unicode

Total characters193
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st rowNauubos ang tubig sa poso
2nd rowTag-init lang
3rd rowBy Schedule
4th rowMalfunction
5th rowMADUMI/MAPUTIK
ValueCountFrequency (%)
malfunction 2
 
7.1%
ang 1
 
3.6%
nauubos 1
 
3.6%
tubig 1
 
3.6%
sa 1
 
3.6%
tag-init 1
 
3.6%
poso 1
 
3.6%
lang 1
 
3.6%
by 1
 
3.6%
schedule 1
 
3.6%
Other values (17) 17
60.7%
2025-07-05T17:36:34.318585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 14
 
7.3%
n 13
 
6.7%
12
 
6.2%
a 12
 
6.2%
o 11
 
5.7%
t 11
 
5.7%
l 8
 
4.1%
u 8
 
4.1%
e 7
 
3.6%
r 7
 
3.6%
Other values (31) 90
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 193
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 14
 
7.3%
n 13
 
6.7%
12
 
6.2%
a 12
 
6.2%
o 11
 
5.7%
t 11
 
5.7%
l 8
 
4.1%
u 8
 
4.1%
e 7
 
3.6%
r 7
 
3.6%
Other values (31) 90
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 193
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 14
 
7.3%
n 13
 
6.7%
12
 
6.2%
a 12
 
6.2%
o 11
 
5.7%
t 11
 
5.7%
l 8
 
4.1%
u 8
 
4.1%
e 7
 
3.6%
r 7
 
3.6%
Other values (31) 90
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 193
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 14
 
7.3%
n 13
 
6.7%
12
 
6.2%
a 12
 
6.2%
o 11
 
5.7%
t 11
 
5.7%
l 8
 
4.1%
u 8
 
4.1%
e 7
 
3.6%
r 7
 
3.6%
Other values (31) 90
46.6%

CWSU-11 - Issues (Truck Water) - Yes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
479 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 479
95.8%
1 21
 
4.2%

Length

2025-07-05T17:36:34.427177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:34.493459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 479
95.8%
1 21
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 479
95.8%
1 21
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 479
95.8%
1 21
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 479
95.8%
1 21
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 479
95.8%
1 21
 
4.2%

CWSU-11 - Issues (Truck Water) - No
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
288 
1
212 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

Length

2025-07-05T17:36:34.575895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:34.647552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

Most occurring characters

ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 288
57.6%
1 212
42.4%

CWSU-11 - Issues (Truck Water) - N/A
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
277 
1
223 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 277
55.4%
1 223
44.6%

Length

2025-07-05T17:36:34.728415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:34.791996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 277
55.4%
1 223
44.6%

Most occurring characters

ValueCountFrequency (%)
0 277
55.4%
1 223
44.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 277
55.4%
1 223
44.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 277
55.4%
1 223
44.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 277
55.4%
1 223
44.6%

CWSU-11 - Issues (Truck Water) - Supply interruption
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
493 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Length

2025-07-05T17:36:34.886436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:35.023475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

CWSU-11 - Issues (Truck Water) - Salinity
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
496 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Length

2025-07-05T17:36:35.158519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:35.264037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

CWSU-11 - Issues (Truck Water) - Taste
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
496 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Length

2025-07-05T17:36:35.388156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:35.490679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

CWSU-11 - Issues (Truck Water) - Smell
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
497 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Length

2025-07-05T17:36:35.632326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:35.811911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

CWSU-11 - Issues (Truck Water) - Turbidity
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
498 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Length

2025-07-05T17:36:36.079371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:36.320339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

CWSU-11 - Issues (Truck Water) - Costs
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
493 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Length

2025-07-05T17:36:36.603364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:36.791897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

CWSU-11 - Issues (Truck Water) - Others
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
496 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Length

2025-07-05T17:36:37.042727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:37.274715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 496
99.2%
1 4
 
0.8%
Distinct4
Distinct (%)100.0%
Missing496
Missing (%)99.2%
Memory size4.0 KiB
2025-07-05T17:36:37.679577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length22
Mean length15.5
Min length9

Characters and Unicode

Total characters62
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowstructures
2nd rowHigh Cost
3rd rowDELEIVERY
4th rowwaiting period for delivery- 3days
ValueCountFrequency (%)
structures 1
11.1%
high 1
11.1%
cost 1
11.1%
deleivery 1
11.1%
waiting 1
11.1%
period 1
11.1%
for 1
11.1%
delivery 1
11.1%
3days 1
11.1%
2025-07-05T17:36:38.659757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 5
 
8.1%
5
 
8.1%
i 5
 
8.1%
e 4
 
6.5%
t 4
 
6.5%
s 4
 
6.5%
E 3
 
4.8%
o 3
 
4.8%
d 3
 
4.8%
u 2
 
3.2%
Other values (21) 24
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 5
 
8.1%
5
 
8.1%
i 5
 
8.1%
e 4
 
6.5%
t 4
 
6.5%
s 4
 
6.5%
E 3
 
4.8%
o 3
 
4.8%
d 3
 
4.8%
u 2
 
3.2%
Other values (21) 24
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 5
 
8.1%
5
 
8.1%
i 5
 
8.1%
e 4
 
6.5%
t 4
 
6.5%
s 4
 
6.5%
E 3
 
4.8%
o 3
 
4.8%
d 3
 
4.8%
u 2
 
3.2%
Other values (21) 24
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 5
 
8.1%
5
 
8.1%
i 5
 
8.1%
e 4
 
6.5%
t 4
 
6.5%
s 4
 
6.5%
E 3
 
4.8%
o 3
 
4.8%
d 3
 
4.8%
u 2
 
3.2%
Other values (21) 24
38.7%

CWSU-12 - Issues (Bottled Water) - Yes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
459 
1
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

Length

2025-07-05T17:36:39.035261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:39.295041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 459
91.8%
1 41
 
8.2%

CWSU-12 - Issues (Bottled Water) - No
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
351 
0
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 351
70.2%
0 149
29.8%

Length

2025-07-05T17:36:39.864904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:39.965379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 351
70.2%
0 149
29.8%

Most occurring characters

ValueCountFrequency (%)
1 351
70.2%
0 149
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 351
70.2%
0 149
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 351
70.2%
0 149
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 351
70.2%
0 149
29.8%

CWSU-12 - Issues (Bottled Water) - N/A
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
445 
1
55 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 445
89.0%
1 55
 
11.0%

Length

2025-07-05T17:36:40.065134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:40.138347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 445
89.0%
1 55
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 445
89.0%
1 55
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 445
89.0%
1 55
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 445
89.0%
1 55
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 445
89.0%
1 55
 
11.0%
Distinct1
Distinct (%)100.0%
Missing499
Missing (%)99.8%
Memory size4.0 KiB
2025-07-05T17:36:40.247741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters26
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowdelivery during wet season
ValueCountFrequency (%)
delivery 1
25.0%
during 1
25.0%
wet 1
25.0%
season 1
25.0%
2025-07-05T17:36:40.510836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4
15.4%
3
11.5%
i 2
 
7.7%
d 2
 
7.7%
r 2
 
7.7%
n 2
 
7.7%
s 2
 
7.7%
l 1
 
3.8%
y 1
 
3.8%
v 1
 
3.8%
Other values (6) 6
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4
15.4%
3
11.5%
i 2
 
7.7%
d 2
 
7.7%
r 2
 
7.7%
n 2
 
7.7%
s 2
 
7.7%
l 1
 
3.8%
y 1
 
3.8%
v 1
 
3.8%
Other values (6) 6
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4
15.4%
3
11.5%
i 2
 
7.7%
d 2
 
7.7%
r 2
 
7.7%
n 2
 
7.7%
s 2
 
7.7%
l 1
 
3.8%
y 1
 
3.8%
v 1
 
3.8%
Other values (6) 6
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4
15.4%
3
11.5%
i 2
 
7.7%
d 2
 
7.7%
r 2
 
7.7%
n 2
 
7.7%
s 2
 
7.7%
l 1
 
3.8%
y 1
 
3.8%
v 1
 
3.8%
Other values (6) 6
23.1%

CWSU-12 - Issues (Bottled Water) - Supply interruption
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
491 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Length

2025-07-05T17:36:40.620428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:40.689107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

CWSU-12 - Issues (Bottled Water) - Salinity
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
498 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Length

2025-07-05T17:36:40.766641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:40.830058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 498
99.6%
1 2
 
0.4%

CWSU-12 - Issues (Bottled Water) - Taste
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
481 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Length

2025-07-05T17:36:40.911331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:40.981576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

CWSU-12 - Issues (Bottled Water) - Smell
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
497 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Length

2025-07-05T17:36:41.062066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:41.133829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 497
99.4%
1 3
 
0.6%
Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 500
100.0%

Length

2025-07-05T17:36:41.223232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:41.316580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 500
100.0%

Most occurring characters

ValueCountFrequency (%)
0 500
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 500
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 500
100.0%

CWSU-12 - Issues (Bottled Water) - Costs
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
490 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

Length

2025-07-05T17:36:41.444617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:41.526844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

CWSU-12 - Issues (Bottled Water) - Others
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
493 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Length

2025-07-05T17:36:41.630393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:41.708905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%
Distinct6
Distinct (%)100.0%
Missing494
Missing (%)98.8%
Memory size4.0 KiB
2025-07-05T17:36:41.859153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length17
Mean length14.833333
Min length7

Characters and Unicode

Total characters89
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st rowMalinis
2nd rowpower interruption
3rd rowSchedule of Delivery
4th rowDelayed Delivery
5th rowinterruption
ValueCountFrequency (%)
interruption 2
18.2%
delivery 2
18.2%
malinis 1
9.1%
schedule 1
9.1%
power 1
9.1%
of 1
9.1%
delayed 1
9.1%
overused 1
9.1%
gallons 1
9.1%
2025-07-05T17:36:42.583574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13
14.6%
r 8
 
9.0%
i 8
 
9.0%
l 7
 
7.9%
o 6
 
6.7%
n 6
 
6.7%
5
 
5.6%
u 4
 
4.5%
t 4
 
4.5%
a 3
 
3.4%
Other values (13) 25
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13
14.6%
r 8
 
9.0%
i 8
 
9.0%
l 7
 
7.9%
o 6
 
6.7%
n 6
 
6.7%
5
 
5.6%
u 4
 
4.5%
t 4
 
4.5%
a 3
 
3.4%
Other values (13) 25
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13
14.6%
r 8
 
9.0%
i 8
 
9.0%
l 7
 
7.9%
o 6
 
6.7%
n 6
 
6.7%
5
 
5.6%
u 4
 
4.5%
t 4
 
4.5%
a 3
 
3.4%
Other values (13) 25
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13
14.6%
r 8
 
9.0%
i 8
 
9.0%
l 7
 
7.9%
o 6
 
6.7%
n 6
 
6.7%
5
 
5.6%
u 4
 
4.5%
t 4
 
4.5%
a 3
 
3.4%
Other values (13) 25
28.1%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
274 
0
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 274
54.8%
0 226
45.2%

Length

2025-07-05T17:36:43.526027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:43.919638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 274
54.8%
0 226
45.2%

Most occurring characters

ValueCountFrequency (%)
1 274
54.8%
0 226
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 274
54.8%
0 226
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 274
54.8%
0 226
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 274
54.8%
0 226
45.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
467 
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 467
93.4%
1 33
 
6.6%

Length

2025-07-05T17:36:44.023610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:44.104613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 467
93.4%
1 33
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 467
93.4%
1 33
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 467
93.4%
1 33
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 467
93.4%
1 33
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 467
93.4%
1 33
 
6.6%

CWSU-13 - Interruptions (days) - Twice a week
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
480 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 480
96.0%
1 20
 
4.0%

Length

2025-07-05T17:36:44.198259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:44.280513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 480
96.0%
1 20
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 480
96.0%
1 20
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 480
96.0%
1 20
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 480
96.0%
1 20
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 480
96.0%
1 20
 
4.0%

CWSU-13 - Interruptions (days) - Thrice a week
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
491 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Length

2025-07-05T17:36:44.385717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:44.467828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%
Distinct24
Distinct (%)55.8%
Missing457
Missing (%)91.4%
Memory size4.0 KiB
2025-07-05T17:36:44.662152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length38
Mean length11.372093
Min length4

Characters and Unicode

Total characters489
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)46.5%

Sample

1st rowNo line of water
2nd rowNo line of water
3rd rowNo line of water
4th rowno water nawasa
5th rowNo line
ValueCountFrequency (%)
summer 20
22.0%
no 7
 
7.7%
line 4
 
4.4%
water 4
 
4.4%
season 4
 
4.4%
of 3
 
3.3%
twice 3
 
3.3%
every 3
 
3.3%
a 2
 
2.2%
late 2
 
2.2%
Other values (35) 39
42.9%
2025-07-05T17:36:44.983301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 58
 
11.9%
48
 
9.8%
m 45
 
9.2%
r 40
 
8.2%
a 27
 
5.5%
u 26
 
5.3%
o 23
 
4.7%
t 22
 
4.5%
S 20
 
4.1%
n 20
 
4.1%
Other values (35) 160
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 58
 
11.9%
48
 
9.8%
m 45
 
9.2%
r 40
 
8.2%
a 27
 
5.5%
u 26
 
5.3%
o 23
 
4.7%
t 22
 
4.5%
S 20
 
4.1%
n 20
 
4.1%
Other values (35) 160
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 58
 
11.9%
48
 
9.8%
m 45
 
9.2%
r 40
 
8.2%
a 27
 
5.5%
u 26
 
5.3%
o 23
 
4.7%
t 22
 
4.5%
S 20
 
4.1%
n 20
 
4.1%
Other values (35) 160
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 58
 
11.9%
48
 
9.8%
m 45
 
9.2%
r 40
 
8.2%
a 27
 
5.5%
u 26
 
5.3%
o 23
 
4.7%
t 22
 
4.5%
S 20
 
4.1%
n 20
 
4.1%
Other values (35) 160
32.7%
Distinct32
Distinct (%)78.0%
Missing459
Missing (%)91.8%
Memory size4.0 KiB
2025-07-05T17:36:45.201362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33
Median length16
Mean length8.1219512
Min length1

Characters and Unicode

Total characters333
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)65.9%

Sample

1st rowOnly Summer
2nd rownatyanan
3rd rowTag-init lang
4th rowWala
5th rowWala
ValueCountFrequency (%)
02-mar 5
 
7.7%
everyday 5
 
7.7%
hrs 4
 
6.2%
a 3
 
4.6%
feb-june 3
 
4.6%
wala 2
 
3.1%
summer 2
 
3.1%
2hrs 2
 
3.1%
2 2
 
3.1%
1-2 2
 
3.1%
Other values (34) 35
53.8%
2025-07-05T17:36:45.581465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 30
 
9.0%
24
 
7.2%
E 21
 
6.3%
r 17
 
5.1%
- 15
 
4.5%
2 14
 
4.2%
n 13
 
3.9%
R 12
 
3.6%
s 10
 
3.0%
h 9
 
2.7%
Other values (41) 168
50.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 30
 
9.0%
24
 
7.2%
E 21
 
6.3%
r 17
 
5.1%
- 15
 
4.5%
2 14
 
4.2%
n 13
 
3.9%
R 12
 
3.6%
s 10
 
3.0%
h 9
 
2.7%
Other values (41) 168
50.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 30
 
9.0%
24
 
7.2%
E 21
 
6.3%
r 17
 
5.1%
- 15
 
4.5%
2 14
 
4.2%
n 13
 
3.9%
R 12
 
3.6%
s 10
 
3.0%
h 9
 
2.7%
Other values (41) 168
50.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 30
 
9.0%
24
 
7.2%
E 21
 
6.3%
r 17
 
5.1%
- 15
 
4.5%
2 14
 
4.2%
n 13
 
3.9%
R 12
 
3.6%
s 10
 
3.0%
h 9
 
2.7%
Other values (41) 168
50.5%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
264 
0
236 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 264
52.8%
0 236
47.2%

Length

2025-07-05T17:36:45.724388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:45.803174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 264
52.8%
0 236
47.2%

Most occurring characters

ValueCountFrequency (%)
1 264
52.8%
0 236
47.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 264
52.8%
0 236
47.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 264
52.8%
0 236
47.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 264
52.8%
0 236
47.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
289 
0
211 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 289
57.8%
0 211
42.2%

Length

2025-07-05T17:36:45.909799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:45.994229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 289
57.8%
0 211
42.2%

Most occurring characters

ValueCountFrequency (%)
1 289
57.8%
0 211
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 289
57.8%
0 211
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 289
57.8%
0 211
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 289
57.8%
0 211
42.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
350 
0
150 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 350
70.0%
0 150
30.0%

Length

2025-07-05T17:36:46.101550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:46.185020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 350
70.0%
0 150
30.0%

Most occurring characters

ValueCountFrequency (%)
1 350
70.0%
0 150
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 350
70.0%
0 150
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 350
70.0%
0 150
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 350
70.0%
0 150
30.0%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
334 
1
166 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 334
66.8%
1 166
33.2%

Length

2025-07-05T17:36:46.295039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:46.369698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 334
66.8%
1 166
33.2%

Most occurring characters

ValueCountFrequency (%)
0 334
66.8%
1 166
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 334
66.8%
1 166
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 334
66.8%
1 166
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 334
66.8%
1 166
33.2%

AWTP-14 - Factors affecting Alt Sources - Others
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
491 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Length

2025-07-05T17:36:46.479278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:46.559239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 491
98.2%
1 9
 
1.8%
Distinct3
Distinct (%)100.0%
Missing497
Missing (%)99.4%
Memory size4.0 KiB
2025-07-05T17:36:47.250169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length23
Mean length25.666667
Min length19

Characters and Unicode

Total characters77
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowstick to usual supplier
2nd rowHindi na magpapalit
3rd rowResidential Houses are over crowded
ValueCountFrequency (%)
stick 1
8.3%
to 1
8.3%
usual 1
8.3%
supplier 1
8.3%
hindi 1
8.3%
na 1
8.3%
magpapalit 1
8.3%
residential 1
8.3%
houses 1
8.3%
are 1
8.3%
Other values (2) 2
16.7%
2025-07-05T17:36:48.557480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
11.7%
a 7
 
9.1%
e 7
 
9.1%
i 7
 
9.1%
s 6
 
7.8%
d 4
 
5.2%
l 4
 
5.2%
t 4
 
5.2%
o 4
 
5.2%
u 4
 
5.2%
Other values (11) 21
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9
11.7%
a 7
 
9.1%
e 7
 
9.1%
i 7
 
9.1%
s 6
 
7.8%
d 4
 
5.2%
l 4
 
5.2%
t 4
 
5.2%
o 4
 
5.2%
u 4
 
5.2%
Other values (11) 21
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9
11.7%
a 7
 
9.1%
e 7
 
9.1%
i 7
 
9.1%
s 6
 
7.8%
d 4
 
5.2%
l 4
 
5.2%
t 4
 
5.2%
o 4
 
5.2%
u 4
 
5.2%
Other values (11) 21
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9
11.7%
a 7
 
9.1%
e 7
 
9.1%
i 7
 
9.1%
s 6
 
7.8%
d 4
 
5.2%
l 4
 
5.2%
t 4
 
5.2%
o 4
 
5.2%
u 4
 
5.2%
Other values (11) 21
27.3%

AWTP-15 - Desalination Awareness - Yes
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
266 
1
234 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 266
53.2%
1 234
46.8%

Length

2025-07-05T17:36:48.682060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:48.776705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 266
53.2%
1 234
46.8%

Most occurring characters

ValueCountFrequency (%)
0 266
53.2%
1 234
46.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 266
53.2%
1 234
46.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 266
53.2%
1 234
46.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 266
53.2%
1 234
46.8%

AWTP-15 - Desalination Awareness - No
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
261 
1
239 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 261
52.2%
1 239
47.8%

Length

2025-07-05T17:36:48.877707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:48.953942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 261
52.2%
1 239
47.8%

Most occurring characters

ValueCountFrequency (%)
0 261
52.2%
1 239
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 261
52.2%
1 239
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 261
52.2%
1 239
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 261
52.2%
1 239
47.8%

AWTP-16 - Desalinated Willingness - Yes
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
369 
0
131 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 369
73.8%
0 131
 
26.2%

Length

2025-07-05T17:36:49.054091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:49.132828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 369
73.8%
0 131
 
26.2%

Most occurring characters

ValueCountFrequency (%)
1 369
73.8%
0 131
 
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 369
73.8%
0 131
 
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 369
73.8%
0 131
 
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 369
73.8%
0 131
 
26.2%

AWTP-16 - Desalinated Willingness - No
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
399 
1
101 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

Length

2025-07-05T17:36:49.248645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:49.327650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

Most occurring characters

ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 399
79.8%
1 101
 
20.2%

AWTP-17 - Desalinated Premium Pay - Yes
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
366 
1
134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 366
73.2%
1 134
 
26.8%

Length

2025-07-05T17:36:49.419063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:49.495116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 366
73.2%
1 134
 
26.8%

Most occurring characters

ValueCountFrequency (%)
0 366
73.2%
1 134
 
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 366
73.2%
1 134
 
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 366
73.2%
1 134
 
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 366
73.2%
1 134
 
26.8%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
383 
1
117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

Length

2025-07-05T17:36:49.623576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:49.739267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

Most occurring characters

ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 383
76.6%
1 117
 
23.4%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
333 
1
167 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 333
66.6%
1 167
33.4%

Length

2025-07-05T17:36:50.908458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:51.028657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 333
66.6%
1 167
33.4%

Most occurring characters

ValueCountFrequency (%)
0 333
66.6%
1 167
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 333
66.6%
1 167
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 333
66.6%
1 167
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 333
66.6%
1 167
33.4%

AWTP-17 - Desalinated Premium Pay - If yes, in what payment structure
Unsupported

Missing  Rejected  Unsupported 

Missing500
Missing (%)100.0%
Memory size4.0 KiB

AWTP-17 - Desalinated Premium Pay - Fixed tariff
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
478 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%

Length

2025-07-05T17:36:51.186983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:51.311285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 478
95.6%
1 22
 
4.4%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
442 
1
58 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 442
88.4%
1 58
 
11.6%

Length

2025-07-05T17:36:51.453524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:51.639419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 442
88.4%
1 58
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 442
88.4%
1 58
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 442
88.4%
1 58
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 442
88.4%
1 58
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 442
88.4%
1 58
 
11.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
421 
1
79 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 421
84.2%
1 79
 
15.8%

Length

2025-07-05T17:36:51.904947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:52.099991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 421
84.2%
1 79
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 421
84.2%
1 79
 
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 421
84.2%
1 79
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 421
84.2%
1 79
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 421
84.2%
1 79
 
15.8%

AWTP-17 - Desalinated Premium Pay - Seasonal
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
476 
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

Length

2025-07-05T17:36:52.380469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:52.634190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 476
95.2%
1 24
 
4.8%

AWTP - Max Price - Commercial - 160
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
283 
1
217 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 283
56.6%
1 217
43.4%

Length

2025-07-05T17:36:52.974514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:53.387082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 283
56.6%
1 217
43.4%

Most occurring characters

ValueCountFrequency (%)
0 283
56.6%
1 217
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 283
56.6%
1 217
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 283
56.6%
1 217
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 283
56.6%
1 217
43.4%

AWTP - Max Price - Commercial - 200
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
455 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 455
91.0%
1 45
 
9.0%

Length

2025-07-05T17:36:53.748405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:54.010689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 455
91.0%
1 45
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 455
91.0%
1 45
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 455
91.0%
1 45
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 455
91.0%
1 45
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 455
91.0%
1 45
 
9.0%

AWTP - Max Price - Commercial - 100
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
466 
1
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 466
93.2%
1 34
 
6.8%

Length

2025-07-05T17:36:54.918311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:55.085938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 466
93.2%
1 34
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 466
93.2%
1 34
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 466
93.2%
1 34
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 466
93.2%
1 34
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 466
93.2%
1 34
 
6.8%
Distinct12
Distinct (%)52.2%
Missing477
Missing (%)95.4%
Memory size4.0 KiB
2025-07-05T17:36:55.197171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.4347826
Min length2

Characters and Unicode

Total characters56
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)30.4%

Sample

1st row20
2nd row80
3rd row300
4th row1,500
5th row100
ValueCountFrequency (%)
30 5
21.7%
35 3
13.0%
100 3
13.0%
25 3
13.0%
300 2
 
8.7%
1,500 1
 
4.3%
20 1
 
4.3%
80 1
 
4.3%
600 1
 
4.3%
40 1
 
4.3%
Other values (2) 2
 
8.7%
2025-07-05T17:36:55.473717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24
42.9%
3 10
17.9%
5 9
 
16.1%
1 5
 
8.9%
2 4
 
7.1%
, 1
 
1.8%
8 1
 
1.8%
6 1
 
1.8%
4 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24
42.9%
3 10
17.9%
5 9
 
16.1%
1 5
 
8.9%
2 4
 
7.1%
, 1
 
1.8%
8 1
 
1.8%
6 1
 
1.8%
4 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24
42.9%
3 10
17.9%
5 9
 
16.1%
1 5
 
8.9%
2 4
 
7.1%
, 1
 
1.8%
8 1
 
1.8%
6 1
 
1.8%
4 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24
42.9%
3 10
17.9%
5 9
 
16.1%
1 5
 
8.9%
2 4
 
7.1%
, 1
 
1.8%
8 1
 
1.8%
6 1
 
1.8%
4 1
 
1.8%

AWTP-19 - Max Price - Residential - 50
Categorical

High correlation  Missing 

Distinct2
Distinct (%)1.4%
Missing360
Missing (%)72.0%
Memory size4.0 KiB
1.0
84 
0.0
56 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters420
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 84
 
16.8%
0.0 56
 
11.2%
(Missing) 360
72.0%

Length

2025-07-05T17:36:55.586925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:55.666362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 84
60.0%
0.0 56
40.0%

Most occurring characters

ValueCountFrequency (%)
0 196
46.7%
. 140
33.3%
1 84
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196
46.7%
. 140
33.3%
1 84
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196
46.7%
. 140
33.3%
1 84
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196
46.7%
. 140
33.3%
1 84
20.0%

AWTP-19 - Max Price - Residential - 80
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)1.4%
Missing360
Missing (%)72.0%
Memory size4.0 KiB
0.0
131 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters420
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 131
 
26.2%
1.0 9
 
1.8%
(Missing) 360
72.0%

Length

2025-07-05T17:36:55.769033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:55.846247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 131
93.6%
1.0 9
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 271
64.5%
. 140
33.3%
1 9
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 271
64.5%
. 140
33.3%
1 9
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 271
64.5%
. 140
33.3%
1 9
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 271
64.5%
. 140
33.3%
1 9
 
2.1%

AWTP-19 - Max Price - Residential - 100
Categorical

High correlation  Missing 

Distinct2
Distinct (%)1.4%
Missing360
Missing (%)72.0%
Memory size4.0 KiB
0.0
121 
1.0
19 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters420
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 121
 
24.2%
1.0 19
 
3.8%
(Missing) 360
72.0%

Length

2025-07-05T17:36:55.955655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:56.040764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 121
86.4%
1.0 19
 
13.6%

Most occurring characters

ValueCountFrequency (%)
0 261
62.1%
. 140
33.3%
1 19
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 261
62.1%
. 140
33.3%
1 19
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 261
62.1%
. 140
33.3%
1 19
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 261
62.1%
. 140
33.3%
1 19
 
4.5%

AWTP-19 - Max Price - Residential - Others
Categorical

High correlation  Missing 

Distinct5
Distinct (%)41.7%
Missing488
Missing (%)97.6%
Memory size4.0 KiB
30
35
20
25
less than 50

Length

Max length12
Median length2
Mean length2.8333333
Min length2

Characters and Unicode

Total characters34
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)25.0%

Sample

1st row35
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30 6
 
1.2%
35 3
 
0.6%
20 1
 
0.2%
25 1
 
0.2%
less than 50 1
 
0.2%
(Missing) 488
97.6%

Length

2025-07-05T17:36:56.150685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:56.253578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
30 6
42.9%
35 3
21.4%
20 1
 
7.1%
25 1
 
7.1%
less 1
 
7.1%
than 1
 
7.1%
50 1
 
7.1%

Most occurring characters

ValueCountFrequency (%)
3 9
26.5%
0 8
23.5%
5 5
14.7%
2 2
 
5.9%
2
 
5.9%
s 2
 
5.9%
e 1
 
2.9%
l 1
 
2.9%
t 1
 
2.9%
h 1
 
2.9%
Other values (2) 2
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 9
26.5%
0 8
23.5%
5 5
14.7%
2 2
 
5.9%
2
 
5.9%
s 2
 
5.9%
e 1
 
2.9%
l 1
 
2.9%
t 1
 
2.9%
h 1
 
2.9%
Other values (2) 2
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 9
26.5%
0 8
23.5%
5 5
14.7%
2 2
 
5.9%
2
 
5.9%
s 2
 
5.9%
e 1
 
2.9%
l 1
 
2.9%
t 1
 
2.9%
h 1
 
2.9%
Other values (2) 2
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 9
26.5%
0 8
23.5%
5 5
14.7%
2 2
 
5.9%
2
 
5.9%
s 2
 
5.9%
e 1
 
2.9%
l 1
 
2.9%
t 1
 
2.9%
h 1
 
2.9%
Other values (2) 2
 
5.9%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
358 
0
142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 358
71.6%
0 142
 
28.4%

Length

2025-07-05T17:36:56.429187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:56.514419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 358
71.6%
0 142
 
28.4%

Most occurring characters

ValueCountFrequency (%)
1 358
71.6%
0 142
 
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 358
71.6%
0 142
 
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 358
71.6%
0 142
 
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 358
71.6%
0 142
 
28.4%

AWTP-20 - Budget - 3000 - 6000
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
481 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Length

2025-07-05T17:36:56.615379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:56.701378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 481
96.2%
1 19
 
3.8%

AWTP-20 - Budget - 6000 - 9000
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
490 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

Length

2025-07-05T17:36:56.799504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:56.872836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 490
98.0%
1 10
 
2.0%

AWTP-20 - Budget - 9000 - 15000
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
492 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 492
98.4%
1 8
 
1.6%

Length

2025-07-05T17:36:56.964875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T17:36:57.044724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 492
98.4%
1 8
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 492
98.4%
1 8
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 492
98.4%
1 8
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 492
98.4%
1 8
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 492
98.4%
1 8
 
1.6%

AWTP-20 - Budget - Others
Categorical

High correlation  Missing 

Distinct19
Distinct (%)38.0%
Missing450
Missing (%)90.0%
Memory size4.0 KiB
500
13 
1,000
12 
1000
250
600
Other values (14)
15 

Length

Max length27
Median length15
Mean length4.76
Min length2

Characters and Unicode

Total characters238
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)26.0%

Sample

1st row600
2nd row500
3rd row500
4th rowDepends on everyday consume
5th row250

Common Values

ValueCountFrequency (%)
500 13
 
2.6%
1,000 12
 
2.4%
1000 4
 
0.8%
250 3
 
0.6%
600 3
 
0.6%
400 2
 
0.4%
Depends on everyday consume 1
 
0.2%
200-300 1
 
0.2%
less than 1,000 1
 
0.2%
1,500 1
 
0.2%
Other values (9) 9
 
1.8%
(Missing) 450
90.0%

Length

2025-07-05T17:36:57.152124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
500 14
24.6%
1,000 13
22.8%
1000 4
 
7.0%
250 3
 
5.3%
600 3
 
5.3%
400 2
 
3.5%
than 2
 
3.5%
less 2
 
3.5%
depends 1
 
1.8%
on 1
 
1.8%
Other values (12) 12
21.1%

Most occurring characters

ValueCountFrequency (%)
0 118
49.6%
5 20
 
8.4%
1 19
 
8.0%
, 16
 
6.7%
7
 
2.9%
e 7
 
2.9%
s 6
 
2.5%
2 6
 
2.5%
n 5
 
2.1%
6 3
 
1.3%
Other values (20) 31
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 118
49.6%
5 20
 
8.4%
1 19
 
8.0%
, 16
 
6.7%
7
 
2.9%
e 7
 
2.9%
s 6
 
2.5%
2 6
 
2.5%
n 5
 
2.1%
6 3
 
1.3%
Other values (20) 31
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 118
49.6%
5 20
 
8.4%
1 19
 
8.0%
, 16
 
6.7%
7
 
2.9%
e 7
 
2.9%
s 6
 
2.5%
2 6
 
2.5%
n 5
 
2.1%
6 3
 
1.3%
Other values (20) 31
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 118
49.6%
5 20
 
8.4%
1 19
 
8.0%
, 16
 
6.7%
7
 
2.9%
e 7
 
2.9%
s 6
 
2.5%
2 6
 
2.5%
n 5
 
2.1%
6 3
 
1.3%
Other values (20) 31
 
13.0%

AWTP-21 - Concerns
Text

Missing 

Distinct108
Distinct (%)78.8%
Missing363
Missing (%)72.6%
Memory size4.0 KiB
2025-07-05T17:36:59.059476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length123
Median length64
Mean length26.416058
Min length2

Characters and Unicode

Total characters3619
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)75.2%

Sample

1st rowMabuti
2nd rowFree water delivery in summer
3rd rowNot sure kasi may laman pa ang pump well
4th rowKung maganda, okay.
5th rowCheap, quality, time distribution
ValueCountFrequency (%)
tubig 29
 
4.4%
water 27
 
4.1%
no 27
 
4.1%
ang 26
 
4.0%
ng 24
 
3.7%
summer 14
 
2.1%
pag 14
 
2.1%
na 12
 
1.8%
of 11
 
1.7%
sa 11
 
1.7%
Other values (262) 460
70.2%
2025-07-05T17:36:59.603871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
521
14.4%
a 451
 
12.5%
n 256
 
7.1%
e 197
 
5.4%
i 189
 
5.2%
t 178
 
4.9%
s 174
 
4.8%
g 173
 
4.8%
o 167
 
4.6%
r 161
 
4.4%
Other values (49) 1152
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3619
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
521
14.4%
a 451
 
12.5%
n 256
 
7.1%
e 197
 
5.4%
i 189
 
5.2%
t 178
 
4.9%
s 174
 
4.8%
g 173
 
4.8%
o 167
 
4.6%
r 161
 
4.4%
Other values (49) 1152
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3619
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
521
14.4%
a 451
 
12.5%
n 256
 
7.1%
e 197
 
5.4%
i 189
 
5.2%
t 178
 
4.9%
s 174
 
4.8%
g 173
 
4.8%
o 167
 
4.6%
r 161
 
4.4%
Other values (49) 1152
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3619
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
521
14.4%
a 451
 
12.5%
n 256
 
7.1%
e 197
 
5.4%
i 189
 
5.2%
t 178
 
4.9%
s 174
 
4.8%
g 173
 
4.8%
o 167
 
4.6%
r 161
 
4.4%
Other values (49) 1152
31.8%

Interactions

2025-07-05T17:35:48.816678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:44.604609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:45.201277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:45.858151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:48.281373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:48.922875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:44.722827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:45.352760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:45.954505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:48.392353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:49.039918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:44.851485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:45.484965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:47.318330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:48.498769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:49.175034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:44.947820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:45.609916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:48.046610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:48.600913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:49.308532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:45.083161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:45.724360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:48.143578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T17:35:48.699051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-05T17:36:59.937612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AWTP - Max Price - Commercial - 100AWTP - Max Price - Commercial - 160AWTP - Max Price - Commercial - 200AWTP-14 - Factors affecting Alt Sources - Cost-effectivenessAWTP-14 - Factors affecting Alt Sources - Costumer service and maintenance supportAWTP-14 - Factors affecting Alt Sources - OthersAWTP-14 - Factors affecting Alt Sources - Reliability (consistent supply)AWTP-14 - Factors affecting Alt Sources - Water Quality (Clean and safe)AWTP-15 - Desalination Awareness - NoAWTP-15 - Desalination Awareness - YesAWTP-16 - Desalinated Willingness - NoAWTP-16 - Desalinated Willingness - YesAWTP-17 - Desalinated Premium Pay - Depends on the cost differenceAWTP-17 - Desalinated Premium Pay - Fixed tariffAWTP-17 - Desalinated Premium Pay - NoAWTP-17 - Desalinated Premium Pay - Pay per useAWTP-17 - Desalinated Premium Pay - SeasonalAWTP-17 - Desalinated Premium Pay - Tiered pricingAWTP-17 - Desalinated Premium Pay - YesAWTP-19 - Max Price - Residential - 100AWTP-19 - Max Price - Residential - 50AWTP-19 - Max Price - Residential - 80AWTP-19 - Max Price - Residential - OthersAWTP-20 - Budget - 3000 - 6000AWTP-20 - Budget - 6000 - 9000AWTP-20 - Budget - 9000 - 15000AWTP-20 - Budget - < 3000AWTP-20 - Budget - OthersBarangayCWSU-1 - Usage - Cleaning & SanitationCWSU-1 - Usage - DrinkingCWSU-1 - Usage - Food preparationCWSU-1 - Usage - Landscaping/IrrigationCWSU-1 - Usage - Manufacturing/ProductionCWSU-1 - Usage - OthersCWSU-10 - Issues (Deep Well) - CostsCWSU-10 - Issues (Deep Well) - N/ACWSU-10 - Issues (Deep Well) - NoCWSU-10 - Issues (Deep Well) - OthersCWSU-10 - Issues (Deep Well) - SalinityCWSU-10 - Issues (Deep Well) - SmellCWSU-10 - Issues (Deep Well) - Supply interruptionCWSU-10 - Issues (Deep Well) - TasteCWSU-10 - Issues (Deep Well) - TurbidityCWSU-10 - Issues (Deep Well) - YesCWSU-11 - Issues (Truck Water) - CostsCWSU-11 - Issues (Truck Water) - N/ACWSU-11 - Issues (Truck Water) - NoCWSU-11 - Issues (Truck Water) - OthersCWSU-11 - Issues (Truck Water) - SalinityCWSU-11 - Issues (Truck Water) - SmellCWSU-11 - Issues (Truck Water) - Supply interruptionCWSU-11 - Issues (Truck Water) - TasteCWSU-11 - Issues (Truck Water) - TurbidityCWSU-11 - Issues (Truck Water) - YesCWSU-12 - Issues (Bottled Water) - CostsCWSU-12 - Issues (Bottled Water) - N/ACWSU-12 - Issues (Bottled Water) - NoCWSU-12 - Issues (Bottled Water) - OthersCWSU-12 - Issues (Bottled Water) - SalinityCWSU-12 - Issues (Bottled Water) - SmellCWSU-12 - Issues (Bottled Water) - Supply interruptionCWSU-12 - Issues (Bottled Water) - TasteCWSU-12 - Issues (Bottled Water) - YesCWSU-13 - Interruptions (days) - Once a weekCWSU-13 - Interruptions (days) - Thrice a weekCWSU-13 - Interruptions (days) - Twice a weekCWSU-13 - Interruptions (days) - We have continuous water supply everydayCWSU-2 - TreatmentCWSU-3 - Current Sources - Bottled water (5 gallons)CWSU-3 - Current Sources - Deep Well (owned)CWSU-3 - Current Sources - OthersCWSU-3 - Current Sources - Tap Water (Water District etc.)CWSU-3 - Current Sources - Truck Delivery Services (5 m3)CWSU-4 - Primary Drinking Source - Bottled water (5 gallons)CWSU-4 - Primary Drinking Source - Deep Well (owned)CWSU-4 - Primary Drinking Source - OthersCWSU-4 - Primary Drinking Source - Tap Water (Water District etc.)CWSU-4 - Primary Drinking Source - Truck Delivery Services (5 m3)CWSU-4 - Primary Drinking Source - galsCWSU-5 - Ave Demand (in cbm)CWSU-6 - Actual Use (m3 / gal) - Deep Well (owned)CWSU-6 - Actual Use (m3 / gal) - Tap Water (Water District etc.)CWSU-7 - Peak Month - EndCWSU-7 - Peak Month - StartCWSU-8 - Monthly Costs - Less than PhP 1,000CWSU-8 - Monthly Costs - Others:CWSU-8 - Monthly Costs - PhP 1,000 - PhP 3,000CWSU-8 - Monthly Costs - PhP 10,000 and aboveCWSU-8 - Monthly Costs - PhP 3,000 - PhP 5,000CWSU-8 - Monthly Costs - PhP 5,000 - PhP 10,000CWSU-8 - Monthly Costs - Truck DeliveryCWSU-9 - Issues (Tap Water) - N/ACWSU-9 - Issues (Tap Water) - NoCWSU-9 - Issues (Tap Water) - OthersCWSU-9 - Issues (Tap Water) - SalinityCWSU-9 - Issues (Tap Water) - SmellCWSU-9 - Issues (Tap Water) - Supply interruptionCWSU-9 - Issues (Tap Water) - TasteCWSU-9 - Issues (Tap Water) - TurbidityCWSU-9 - Issues (Tap Water) - YesClassificationGI - Business Location/AddressGI - DateGI - InterviewerGI - MembersGI - Operating Hours - EndGI - Operating Hours - StartGI - Remarks (Small, Medium, Large, Residential)GI - Type of Business
AWTP - Max Price - Commercial - 1001.0000.2240.0550.0320.0460.0000.0000.0680.0800.1120.0000.0000.0000.0000.0470.1020.0000.0000.1440.3070.1780.0001.0000.0800.0000.0000.0000.3500.1430.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0790.0000.1000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0530.0000.0000.0000.0000.0000.0000.0000.1620.0000.0310.0000.0000.0000.0000.1500.0000.0000.0000.0000.0000.0000.0000.0000.5700.2910.3460.0500.0000.0000.0630.0000.0000.9290.0000.0480.0000.0000.0000.0000.0000.0000.0000.0550.1900.1160.2580.0000.0000.0000.0000.282
AWTP - Max Price - Commercial - 1600.2241.0000.2650.1460.1340.0580.1580.0610.0930.0000.0000.0510.0730.0870.0000.0740.0370.0000.0410.0480.0570.0001.0000.0000.0000.0000.1370.3780.5660.0520.0000.0000.0000.0300.0440.0000.0000.1180.0000.0000.0430.0000.0000.0000.0430.0000.0000.0730.0340.0000.0000.0000.0000.0000.0000.0280.0000.0780.0280.0000.0000.0000.0650.0980.0000.0580.0000.1180.0000.0280.0000.0280.0310.0000.0320.0000.0000.0000.0000.2260.0890.0000.0000.1610.1080.0000.0150.0980.0000.0000.0000.8540.0130.0000.0000.0000.0000.0000.0000.0300.0000.4140.4400.3790.3610.1700.1270.0720.4620.212
AWTP - Max Price - Commercial - 2000.0550.2651.0000.0090.0000.0000.0000.0000.0000.0430.0440.0890.0000.0000.0000.0000.0000.0560.0000.0000.0820.3321.0000.1310.0000.0000.0000.0000.2420.0400.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.1020.0000.0000.0340.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.1010.0000.0310.0000.0000.1060.0000.0000.0000.0000.0940.0000.1130.0000.0000.0000.2330.0000.0330.0000.0000.0260.0800.0000.5910.1200.0790.0000.0000.0000.0000.0000.0000.0250.1010.1310.2010.0850.0000.0000.1680.1040.140
AWTP-14 - Factors affecting Alt Sources - Cost-effectiveness0.0320.1460.0091.0000.5250.0870.4600.3530.0490.0000.0000.0410.0890.0610.0000.0000.1200.0580.0000.1060.2550.0380.0000.0000.0450.0950.0360.4420.4660.1930.0730.1390.0000.0220.0000.0000.0000.0000.0000.0110.0510.0000.1200.1140.0000.0420.0840.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0820.0000.0850.0000.0000.0320.0000.0460.0580.0000.0220.0400.1170.0810.0000.0000.0000.1760.0000.0000.1210.1960.0990.0000.0000.0370.0610.0000.0000.1180.0450.0000.0100.0000.0470.0000.0000.0000.0270.0000.4820.1890.4300.0000.0730.0000.2360.136
AWTP-14 - Factors affecting Alt Sources - Costumer service and maintenance support0.0460.1340.0000.5251.0000.0160.4330.3240.0740.0000.0000.0370.0780.0980.0000.0000.1640.0530.0620.0630.0000.0000.2190.0000.0490.0440.0440.5280.4680.2060.0970.1430.0600.1110.0000.0000.0860.0000.0000.0000.0000.0610.0780.1300.1030.0000.0450.0390.0000.0330.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0820.0400.0000.0000.0000.0000.0500.0930.0450.0000.0000.0000.0440.1890.0000.0760.0000.1790.0000.0420.0000.1850.0820.0290.0000.0000.0080.0730.0190.0000.0000.0510.0210.0000.0000.0000.0190.0000.0000.0000.4850.1670.4740.1830.0000.0000.2220.168
AWTP-14 - Factors affecting Alt Sources - Others0.0000.0580.0000.0870.0161.0000.1360.1510.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0810.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0571.0000.0000.0000.0570.1110.0000.0000.0000.0790.9290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1740.3110.0000.0000.0000.000
AWTP-14 - Factors affecting Alt Sources - Reliability (consistent supply)0.0000.1580.0000.4600.4330.1361.0000.2810.0000.0890.0000.1130.0000.0600.0000.0000.1570.0230.0000.0000.0000.0000.0000.0000.0000.0810.0940.4340.2980.1610.1190.1070.0520.0480.0000.0000.0730.0000.0000.0740.0000.1060.1170.1190.1180.0000.1020.0590.0300.0000.0000.0000.0000.0000.0160.0000.0000.0510.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.1080.1360.0000.0000.0000.1200.1450.0000.0400.0000.1760.0490.0000.0000.1610.0000.0650.0000.0000.0830.0000.0000.2280.0000.0000.0000.0000.0000.0320.0000.0000.0000.0390.3040.1260.2940.0000.0000.0520.0670.149
AWTP-14 - Factors affecting Alt Sources - Water Quality (Clean and safe)0.0680.0610.0000.3530.3240.1510.2811.0000.0000.1210.0350.2060.0000.0490.0000.0230.1080.1560.0000.0000.0000.0000.3870.0000.0000.0490.1680.1350.3460.1930.1380.2250.0000.0000.0000.0000.0530.0000.0000.0290.0400.1160.0320.0000.0980.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.1340.0000.0000.0000.0000.0230.0000.0000.0000.0000.0830.0000.0240.1150.0000.0640.0000.1940.0600.0000.0520.0000.2260.0000.0000.3170.1170.0000.0000.0000.0590.0000.0250.0000.0590.0200.0000.0000.0000.0000.0540.0000.0000.0220.0700.3620.1430.2140.0000.0000.0000.1520.275
AWTP-15 - Desalination Awareness - No0.0800.0930.0000.0490.0740.0000.0000.0001.0000.8930.2780.1940.0000.0000.2090.0660.0350.0000.1330.0000.0000.0000.0000.0000.0000.0000.0350.0700.2850.0000.1050.0000.0000.0400.0000.0000.0000.0720.0000.0920.0470.0400.0000.0000.0000.0440.0450.0000.0450.0000.0000.0000.0000.0000.0240.0470.0190.0920.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.1570.0620.0570.0000.0430.0000.0200.0970.0360.0000.0000.1130.0760.1630.0050.0920.1570.0000.0000.0000.0380.0000.0560.0000.0000.0000.0000.0920.0500.0100.0820.0000.0920.0820.2900.0000.3260.3040.0990.0000.1550.223
AWTP-15 - Desalination Awareness - Yes0.1120.0000.0430.0000.0000.0000.0890.1210.8931.0000.2230.3140.0660.0000.1470.0950.0000.0000.1740.0000.0000.0000.3870.0000.0270.0340.0280.0700.2860.0950.0000.0530.0000.0150.0000.0000.0000.0000.0000.1100.0000.0810.0000.0000.0330.0610.0330.0240.0580.0000.0000.0000.0000.0000.0580.0670.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0340.0630.0780.0000.0000.0930.0870.1580.0050.0720.1500.0000.0000.0270.0690.0000.0230.0000.0000.0240.0000.1100.0750.0730.1020.0130.1340.1170.2760.0680.3220.2650.0750.0000.1600.227
AWTP-16 - Desalinated Willingness - No0.0000.0000.0440.0000.0000.0000.0000.0350.2780.2231.0000.8160.0980.0000.4200.1360.0910.0860.2390.0000.0000.1080.0000.0000.0000.0000.0000.0000.2740.0000.0000.0000.0000.0000.0000.0000.0000.0940.0200.0380.0000.0520.0000.0290.0460.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0940.0560.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.2280.0170.1500.0000.0000.0000.0000.0220.0000.0000.0000.0000.0750.0000.0000.0710.0710.0000.0000.0790.0000.2090.0000.2920.0000.0000.0890.0000.000
AWTP-16 - Desalinated Willingness - Yes0.0000.0510.0890.0410.0370.0000.1130.2060.1940.3140.8161.0000.1300.0230.3400.1460.1150.1450.2800.0000.1210.0420.0000.0380.0000.0000.1160.0000.3230.1590.0780.1280.0790.0400.0000.0000.0300.0000.0000.0590.0000.0540.0000.0590.0570.0000.0050.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0260.0000.0000.0000.0380.0930.0000.0000.0000.0610.0000.1510.0000.0000.0000.0000.1660.0000.0000.0000.0000.1580.0000.1050.0000.1160.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0650.0870.0000.0000.0990.0000.2720.1020.2900.0950.0560.1350.0000.000
AWTP-17 - Desalinated Premium Pay - Depends on the cost difference0.0000.0730.0000.0890.0780.0000.0000.0000.0000.0660.0980.1301.0000.1340.3840.0000.1420.1230.3640.0000.0090.0000.0000.0000.0000.0000.0600.3640.3330.0220.1200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1130.0000.0000.0000.0200.0000.0330.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0160.0000.0380.0000.0000.0000.0000.0600.0170.0000.0000.0000.0550.0000.0000.0000.0000.1040.1300.0910.0000.1560.0710.1590.0000.0000.0000.0000.0000.0000.3150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2900.0920.3700.0000.0000.0230.1320.205
AWTP-17 - Desalinated Premium Pay - Fixed tariff0.0000.0870.0000.0610.0980.0000.0600.0490.0000.0000.0000.0230.1341.0000.0970.0280.0000.0440.1150.1220.0000.0001.0000.0000.0000.0000.0680.0000.1760.0350.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.1230.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0780.0500.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0001.0000.0000.0880.0000.0000.1140.0000.0000.0000.0000.0000.0000.0000.0000.1350.0000.0350.0000.0540.0000.0000.0000.2270.0000.1760.1930.0000.000
AWTP-17 - Desalinated Premium Pay - No0.0470.0000.0000.0000.0000.0210.0000.0000.2090.1470.4200.3400.3840.0971.0000.2160.1040.1570.3260.1320.1650.0000.0000.0000.0430.0260.0000.4750.3610.0830.0000.0000.0000.0000.0000.0000.0000.1050.0000.0000.0000.0750.0140.0000.0840.0000.0690.1550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0570.0290.0420.0000.0000.0540.0710.0860.0930.0080.0110.0000.0000.0000.0000.0630.0000.1530.0580.0000.0000.2040.1460.0000.0000.0000.0000.0000.0240.0000.0000.1250.0000.0000.0000.0810.0000.0000.0540.0000.3410.2250.4090.1300.0000.0000.0000.000
AWTP-17 - Desalinated Premium Pay - Pay per use0.1020.0740.0000.0000.0000.0000.0000.0230.0660.0950.1360.1460.0000.0280.2161.0000.0380.1230.3730.0000.0000.0000.8370.0560.0000.0000.0000.1800.4470.0980.0720.0220.0000.0610.0000.0000.0650.0000.0810.0000.0000.0000.0000.0000.0000.1020.0000.0000.0000.0290.1370.0000.0290.0930.1350.0000.0340.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.1110.0000.0000.0000.0000.1090.0910.0390.0000.0600.0000.0180.2970.0000.0000.1600.1990.0000.0000.0430.0300.0470.0000.3900.0000.0000.0000.0000.0640.0000.0000.0000.0000.0780.3190.2070.3570.0000.1500.0360.1770.269
AWTP-17 - Desalinated Premium Pay - Seasonal0.0000.0370.0000.1200.1640.0000.1570.1080.0350.0000.0910.1150.1420.0000.1040.0381.0000.0000.0000.0000.1170.0000.0000.0000.0000.0000.0001.0000.7530.0470.0380.1210.0530.1670.0000.0000.0000.0190.0000.0000.0000.0000.1400.1850.0380.0000.0000.0980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.1700.1120.0000.0000.0000.1570.0900.0920.0000.0000.0140.0000.0850.0000.0000.0000.0000.2080.0000.2840.0000.0000.0000.1010.0000.0000.0000.0000.0000.1330.0000.0980.0000.0000.0000.0000.0000.0000.0000.0000.7880.1730.7920.0000.0000.0000.0000.111
AWTP-17 - Desalinated Premium Pay - Tiered pricing0.0000.0000.0560.0580.0530.0000.0230.1560.0000.0000.0860.1450.1230.0440.1570.1230.0001.0000.0870.1710.1680.0000.0000.0000.0000.0000.1010.0000.4240.1030.0390.1070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0980.0000.0280.0600.0000.0990.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.1960.0810.0330.0000.0370.0080.0710.0840.0000.0810.0000.1490.0000.0000.0000.0000.0990.0340.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0150.0550.0000.0000.0000.0000.3690.1750.3540.0000.4320.4250.1360.000
AWTP-17 - Desalinated Premium Pay - Yes0.1440.0410.0000.0000.0620.0000.0000.0000.1330.1740.2390.2800.3640.1150.3260.3730.0000.0871.0000.2340.0000.0620.8370.0000.0380.0000.0000.1370.2900.0930.0900.0000.0000.0470.0000.0000.0000.0000.0350.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0720.0000.0210.0000.0000.0000.0000.0000.0000.0000.0140.0620.0000.0490.0000.0410.0000.0000.0000.0890.0000.0530.0340.0000.0000.0000.0000.1000.0000.0000.0280.0650.1300.0980.0360.0000.1170.0400.2710.1480.2730.0990.0310.1840.0000.037
AWTP-19 - Max Price - Residential - 1000.3070.0480.0000.1060.0630.0000.0000.0000.0000.0000.0000.0000.0000.1220.1320.0000.0000.1710.2341.0000.4580.0001.0001.0001.0001.0000.0000.6200.3490.0000.0000.0000.0000.0001.0000.0000.0000.0000.0950.0000.0000.0680.0000.0000.0350.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0940.1500.0851.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.2500.0001.0001.0000.1010.1200.1221.0000.0000.0000.0000.0000.3250.1220.0000.0850.0000.1310.0000.0000.0000.0001.0000.3680.0760.0000.0750.0000.0001.0000.000
AWTP-19 - Max Price - Residential - 500.1780.0570.0820.2550.0000.0000.0000.0000.0000.0000.0000.1210.0090.0000.1650.0000.1170.1680.0000.4581.0000.2801.0001.0001.0001.0000.2910.0000.5090.0000.0740.0000.0000.1261.0000.0000.0000.1260.0000.0000.0000.0750.0000.0000.1440.0000.1110.0000.0000.0000.0001.0000.0000.0000.0820.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0140.0000.2090.0000.0000.0001.0000.0000.0000.0000.0000.0140.0000.0190.0930.0001.0001.0000.0000.3470.0401.0000.0000.0000.0460.0000.0000.0000.0000.1440.0000.0000.0000.0190.0000.0001.0000.4380.0700.5090.0120.0000.0001.0000.000
AWTP-19 - Max Price - Residential - 800.0000.0000.3320.0380.0000.0000.0000.0000.0000.0000.1080.0420.0000.0000.0000.0000.0000.0000.0620.0000.2801.0001.0001.0001.0001.0000.0001.0000.2960.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.1450.0000.0221.0000.0000.0000.0000.0000.0000.0000.0000.1430.0001.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.1000.2070.1800.0000.0000.0001.0000.000
AWTP-19 - Max Price - Residential - Others1.0001.0001.0000.0000.2191.0000.0000.3870.0000.3870.0000.0000.0001.0000.0000.8370.0000.0000.8371.0001.0001.0001.0001.0001.0001.0000.0001.0000.4660.0000.3871.0001.0000.0001.0001.0000.8370.0000.8371.0000.0001.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.3661.0001.0001.0000.0000.4440.4440.0001.0001.0000.8370.0000.0000.0001.0000.0000.0000.0001.0001.0000.0001.0001.0000.0001.000NaN0.0000.5770.5411.0000.0001.0001.0001.0001.0000.0000.0001.0001.0001.0000.0001.0001.0000.0001.0000.3670.3770.4080.0000.0000.0001.0000.000
AWTP-20 - Budget - 3000 - 60000.0800.0000.1310.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0560.0000.0000.0001.0001.0001.0001.0001.0000.0000.0000.3011.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0660.0000.0610.0000.0000.1950.0000.0000.0000.0000.0480.0000.0000.4500.0000.0140.1530.1570.0000.0000.0000.2490.0860.5910.0350.0000.0000.0000.0000.0000.0490.0000.0000.1030.0760.0000.0001.0000.0000.0000.1970.023
AWTP-20 - Budget - 6000 - 90000.0000.0000.0000.0450.0490.0000.0000.0000.0000.0270.0000.0000.0000.0000.0430.0000.0000.0000.0381.0001.0001.0001.0000.0001.0000.0000.2061.0000.0540.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0480.0750.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3700.0570.0000.0000.0000.0680.0000.0000.0000.0000.2290.5910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.4840.0890.0001.0000.0000.0000.3610.438
AWTP-20 - Budget - 9000 - 150000.0000.0000.0000.0950.0440.0000.0810.0490.0000.0340.0000.0000.0000.0000.0260.0000.0000.0000.0001.0001.0001.0001.0000.0000.0001.0000.1791.0000.0690.0000.0000.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0420.0000.0640.0000.0280.0640.0000.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1320.0000.0000.0000.0000.0000.0000.2590.2880.5720.0630.2880.0840.0000.0000.3980.0000.0000.9290.0350.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0001.0000.1670.0000.3150.000
AWTP-20 - Budget - < 30000.0000.1370.0000.0360.0440.0000.0940.1680.0350.0280.0000.1160.0600.0680.0000.0000.0000.1010.0000.0000.2910.0000.0000.3010.2060.1791.0000.3830.3620.0220.2240.0760.0000.0000.0000.0000.0000.1250.0600.0000.0580.0240.0170.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0320.1710.0000.0000.0000.0000.0000.0000.0530.0000.0000.1850.0000.1790.1530.0000.0540.0930.1860.0190.0240.0000.0000.1680.1760.3290.0000.1360.0000.3270.0000.0910.1500.0000.0840.3380.0390.0750.0810.0000.0000.0000.0000.0000.0000.1020.3080.2270.3540.0000.0000.0000.2160.166
AWTP-20 - Budget - Others0.3500.3780.0000.4420.5281.0000.4340.1350.0700.0700.0000.0000.3640.0000.4750.1801.0000.0000.1370.6200.0001.0001.0001.0001.0001.0000.3831.0000.4880.2320.2740.0001.0000.3981.0001.0000.0000.1400.0000.8041.0000.0000.8040.8040.3211.0000.1480.0001.0001.0001.0001.0001.0001.0000.8040.8040.0000.4161.0001.0001.0001.0000.8040.8040.0000.8040.0000.3970.4140.0000.0001.0000.0910.2420.2060.0000.0000.4780.8040.0001.0000.2971.0000.2170.3230.2600.0000.0000.8040.0000.8040.5000.0000.0000.0001.0001.0000.1271.0001.0000.0000.6450.5360.4100.2270.0000.0000.0000.5530.532
Barangay0.1430.5660.2420.4660.4680.0810.2980.3460.2850.2860.2740.3230.3330.1760.3610.4470.7530.4240.2900.3490.5090.2960.4660.0000.0540.0690.3620.4881.0000.4800.3760.3110.0950.3810.0000.1900.3510.3550.2450.0000.2820.0760.3510.3570.1590.0460.5220.4820.0000.0000.0000.0000.0000.0940.2580.0000.2170.3240.2050.0000.0000.0000.4260.3210.1090.0570.0200.5510.5420.3360.2620.0000.2050.3850.3180.2640.0730.2050.2340.4140.1470.0000.0000.2650.2870.4200.0350.2500.0540.1470.0000.6710.4770.4020.2520.0430.2460.1990.1800.0000.2080.8100.8180.5140.7740.0790.1350.1400.4490.272
CWSU-1 - Usage - Cleaning & Sanitation0.0000.0520.0400.1930.2060.0000.1610.1930.0000.0950.0000.1590.0220.0350.0830.0980.0470.1030.0930.0000.0000.0000.0000.0000.0000.0000.0220.2320.4801.0000.0430.1060.0620.0420.0250.0000.0630.0650.0350.0000.0000.1080.0470.0750.1020.0000.1020.1590.0000.0000.0000.0000.0000.0000.0000.0390.0000.0960.0000.0000.0000.0000.0830.0690.0000.0310.0000.0000.0000.0590.1730.0000.0880.0380.1610.0320.0000.0000.0000.2340.0000.0000.2610.1620.0000.0520.0000.0420.0350.0000.0000.0320.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.5050.2170.5480.0000.0000.0000.0000.250
CWSU-1 - Usage - Drinking0.0000.0000.0000.0730.0970.0000.1190.1380.1050.0000.0000.0780.1200.0000.0000.0720.0380.0390.0900.0000.0740.0000.3870.0000.0000.0000.2240.2740.3760.0431.0000.3970.0000.1120.0000.0000.0640.0000.0450.0430.0450.0000.0380.0480.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0410.0000.0770.0290.0000.0000.0770.0000.0430.0580.2370.1010.0000.0000.0270.1080.1380.0850.0000.0380.2130.1200.3800.2920.0000.0640.0650.0000.0000.0000.0000.0700.0000.0000.0630.0000.0430.0000.0000.0000.0000.0110.1740.3610.1990.3110.0000.0000.1450.2300.370
CWSU-1 - Usage - Food preparation0.0140.0000.0000.1390.1430.0000.1070.2250.0000.0530.0000.1280.0000.0000.0000.0220.1210.1070.0000.0000.0000.0001.0000.0000.0490.0000.0760.0000.3110.1060.3971.0000.0590.1280.0000.0000.0640.0820.0000.0000.0000.0650.0990.0860.0920.0000.0000.0240.0000.0330.0000.0000.0000.0000.0000.0000.0550.1520.0000.0000.0000.0000.0430.0000.0390.0000.0000.0670.1150.1890.2140.0000.0000.0000.1380.1160.0720.0540.0260.1700.0000.2140.0000.1430.0990.0000.0370.0000.0370.0000.0550.4370.0000.1110.0000.0000.0000.0000.0000.0000.0000.2420.2560.2050.2520.0690.0820.1910.4140.560
CWSU-1 - Usage - Landscaping/Irrigation0.0000.0000.0340.0000.0600.0000.0520.0000.0000.0000.0000.0790.0000.0000.0000.0000.0530.0000.0000.0000.0000.0001.0000.0000.0000.0810.0001.0000.0950.0620.0000.0591.0000.1890.0180.0000.0000.0000.0000.1310.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.1030.0000.0000.0390.0000.0300.0000.0960.0000.2030.4020.0480.3970.0000.0000.0250.0390.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.3310.0460.1550.0000.0000.0000.3710.417
CWSU-1 - Usage - Manufacturing/Production0.0000.0300.0000.0220.1110.0000.0480.0000.0400.0150.0000.0400.0000.0000.0000.0610.1670.0000.0470.0000.1260.0000.0000.0000.0000.0000.0000.3980.3810.0420.1120.1280.1891.0000.0000.0000.0340.0000.0000.0000.0000.0000.0700.1180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.1380.0790.0000.0000.0000.0650.0000.0680.0000.0000.0000.0820.0000.0000.0000.0000.0810.2710.0000.0000.0000.1060.0770.1430.0000.0230.0000.0460.0000.0000.0720.0310.0000.0000.0000.0000.0000.0000.0470.1220.4490.2110.3090.1670.0000.0000.1900.094
CWSU-1 - Usage - Others0.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.0000.0250.0000.0000.0180.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.2680.000
CWSU-10 - Issues (Deep Well) - Costs0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.1900.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0440.0000.2430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0590.0000.0000.0000.1860.0000.9881.0001.0000.6820.9830.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0001.0000.0000.0000.0000.641
CWSU-10 - Issues (Deep Well) - N/A0.0000.0000.0000.0000.0860.0360.0730.0530.0000.0000.0000.0300.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.8370.0000.0000.0000.0000.0000.3510.0630.0640.0640.0000.0340.0000.0001.0000.4600.0000.0000.0220.0730.0600.0360.1660.1200.1670.1720.1220.0000.0000.0000.0000.1060.1600.0190.1850.0710.1200.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.3230.0640.1140.2210.0000.1160.0470.0000.0540.0000.0840.0000.5520.1280.0780.0000.0000.0000.0580.0000.0000.4780.1600.0370.0000.0000.0480.0000.0000.0200.0000.0000.3240.1160.3450.0000.0000.1240.0450.118
CWSU-10 - Issues (Deep Well) - No0.0000.1180.0000.0000.0000.0000.0000.0000.0720.0000.0940.0000.0000.0000.1050.0000.0190.0000.0000.0000.1260.0000.0000.0000.0000.0000.1250.1400.3550.0650.0000.0820.0000.0000.0000.0000.4601.0000.1520.1590.2600.3460.2540.2070.5360.1160.1670.3360.0710.0710.0490.0780.0000.0000.1520.0000.1320.3240.0780.0000.0490.0720.0630.1650.1030.0720.1210.3150.0590.0000.3700.0330.0510.2030.0430.0860.0310.0000.0740.0900.0000.1010.1680.2340.1310.1050.0000.0000.0500.0000.0630.1930.0250.3450.0000.1000.1700.1190.0660.0890.2100.0000.3470.1550.2700.1180.0000.0000.1550.176
CWSU-10 - Issues (Deep Well) - Others0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0810.0000.0000.0350.0950.0000.0000.8370.0000.0000.0000.0600.0000.2450.0350.0450.0000.0000.0000.0000.0000.0000.1521.0000.0000.1340.0790.0000.0000.2600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0510.0000.0000.0390.0000.0000.0000.0000.0000.0980.0600.0000.0360.0000.0000.0400.0000.0460.0000.0000.0000.0000.0000.0000.0000.9220.2410.1340.0000.0000.0000.0000.0000.0000.7190.0000.0610.1460.0000.0000.0000.0000.0000.0280.0620.1820.1790.0720.0000.0000.0000.0000.058
CWSU-10 - Issues (Deep Well) - Salinity0.0000.0000.0000.0110.0000.0000.0740.0290.0920.1100.0380.0590.0000.0000.0000.0000.0000.0000.0170.0000.0000.0001.0000.0000.0000.0000.0000.8040.0000.0000.0430.0000.1310.0000.0000.0880.0000.1590.0001.0000.1140.1210.2500.0720.2710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2180.0340.1180.0000.0880.0000.0000.0000.1730.0870.0000.0000.0530.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.1020.0000.4120.0000.7620.2480.4190.0000.0000.0000.0510.0000.0000.0000.0000.1060.0000.2050.0670.1280.0940.0290.1230.0000.3060.1450.3040.0000.0000.0000.0730.715
CWSU-10 - Issues (Deep Well) - Smell0.0000.0430.0000.0510.0000.0000.0000.0400.0470.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0581.0000.2820.0000.0450.0000.0000.0000.0000.0000.0220.2600.1340.1141.0000.1060.2670.1270.4650.0000.1080.0620.0000.0000.0000.0000.0000.0000.0000.0000.0250.0420.0000.0000.0000.0570.0590.0190.1740.0000.1080.1640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.1170.3360.5720.0990.1550.0520.0000.0000.0000.0000.0000.0000.0000.1240.0000.0000.3720.0000.1580.1060.1180.1250.2760.2040.1800.0000.0000.0000.2100.000
CWSU-10 - Issues (Deep Well) - Supply interruption0.0000.0000.0000.0000.0610.0000.1060.1160.0400.0810.0520.0540.0000.0000.0750.0000.0000.0980.0000.0680.0750.0001.0000.0000.0750.0000.0240.0000.0760.1080.0000.0650.0000.0000.0000.0000.0730.3460.0790.1210.1061.0000.1120.0720.5870.0000.0430.0360.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.1450.0000.0000.1340.0860.0000.1080.0550.0000.1280.0000.0000.0000.0000.0200.0000.0000.0000.1610.0000.0000.2740.2100.0000.0150.0000.0000.0000.0130.1830.0000.0000.1700.0000.2240.0000.2370.0000.0000.2560.0000.2870.0000.0000.0000.2010.0950.1160.000
CWSU-10 - Issues (Deep Well) - Taste0.0000.0000.0000.1200.0780.0000.1170.0320.0000.0000.0000.0000.0000.0000.0140.0000.1400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.8040.3510.0470.0380.0990.0530.0700.0000.0000.0600.2540.0000.2500.2670.1121.0000.4980.4540.0000.0280.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.2700.1830.0000.0600.0000.1570.0000.0470.0470.0000.0000.0520.0000.0000.0000.0000.0000.0000.0000.0860.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.1580.0000.0430.2800.0180.2830.1100.1260.1340.4900.1840.4060.0000.0000.0000.0900.000
CWSU-10 - Issues (Deep Well) - Turbidity0.0000.0000.0000.1140.1300.0000.1190.0000.0000.0000.0290.0590.1130.0000.0000.0000.1850.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.8040.3570.0750.0480.0860.0000.1180.0000.0000.0360.2070.0000.0720.1270.0720.4981.0000.3950.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2180.1170.0000.0000.0920.1210.0000.1000.0830.0000.0790.0000.0830.0000.0000.0000.0000.0710.1470.1641.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0310.1190.0000.0000.1030.0000.1360.0000.0000.0500.6720.0730.3640.0000.2680.0000.2430.132
CWSU-10 - Issues (Deep Well) - Yes0.0000.0430.0000.0000.1030.0000.1180.0980.0000.0330.0460.0570.0000.0000.0840.0000.0380.0600.0000.0350.1440.0000.0000.0000.0500.0000.0000.3210.1590.1020.0000.0920.0000.0000.0000.0000.1660.5360.2600.2710.4650.5870.4540.3951.0000.0000.1440.1320.0000.0000.0000.0000.0000.0000.0000.0000.0730.1670.0000.0000.0000.1090.1380.1970.1390.1090.1290.2150.0000.0340.0940.0000.0540.0000.0040.0550.0000.0000.0000.0000.0000.0490.2170.1310.0000.0000.0000.0000.0000.0000.0430.0000.0650.2750.0000.0830.2060.1640.0990.0000.2450.1020.3160.1330.1090.0000.1030.0000.1270.073
CWSU-11 - Issues (Truck Water) - Costs0.0530.0000.0000.0420.0000.0000.0000.0000.0440.0610.0000.0000.0000.0000.0000.1020.0000.0000.0000.0000.0000.0001.0000.0000.0000.0280.0001.0000.0460.0000.0000.0000.0000.0000.0000.0000.1200.1160.0000.0000.0000.0000.0000.0000.0001.0000.0330.0720.0000.4650.0910.0370.2730.1190.4400.0000.0000.0000.0370.0000.0000.0000.0000.0360.0000.0000.0000.0000.0140.0000.1100.0000.0000.2400.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0740.0000.0110.0880.0000.0000.9290.0000.0240.0000.0000.0000.0000.0000.0580.0000.0000.0000.0230.0000.2190.0000.0000.0000.000
CWSU-11 - Issues (Truck Water) - N/A0.0790.0000.0640.0840.0450.0000.1020.0340.0450.0330.0000.0050.0000.1230.0690.0000.0000.0990.0000.0000.1110.0001.0000.0000.0000.0000.0000.1480.5220.1020.0000.0000.0000.0000.0000.0000.1670.1670.0000.0000.1080.0430.0280.0000.1440.0331.0000.7650.0000.0000.0000.0330.0370.0000.1720.0000.2270.1520.0000.0000.0000.0000.0000.0000.0270.0000.0000.0240.0830.0820.0440.0000.0450.1450.0000.0400.0830.0000.0000.0530.0000.1080.1210.2060.1450.0970.0820.0730.0000.0000.0000.2380.3610.2660.0000.0000.0000.0000.0000.0000.0000.1650.5250.1270.5040.0000.0000.1390.2370.053
CWSU-11 - Issues (Truck Water) - No0.0000.0730.0000.0810.0390.0250.0590.0000.0000.0240.0750.0000.0200.0610.1550.0000.0980.0460.0000.0000.0000.0001.0000.0260.0000.0420.0640.0000.4820.1590.0240.0240.0000.0000.0000.0000.1720.3360.0000.0000.0620.0360.0760.0700.1320.0720.7651.0000.0310.0310.0000.0240.0310.0000.1640.0000.1730.2590.0000.0000.0000.0000.0300.0360.0000.0000.0000.1080.0000.0460.0500.0000.0140.0220.0440.0000.0250.0000.0000.1170.0000.0000.0000.1760.2040.0000.0250.0500.0330.0000.0000.1810.2630.4120.0000.0000.0000.0000.0000.0480.0460.0280.5080.0000.5050.0000.0000.1800.1930.088
CWSU-11 - Issues (Truck Water) - Others0.1000.0340.0000.0000.0000.0000.0300.0000.0450.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1220.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0311.0000.0000.0000.0720.0000.1660.2570.0000.0000.0460.0720.0000.0000.0570.0000.0000.1030.0000.0000.0620.0000.0000.0370.0000.0000.0540.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0640.0000.0000.0000.0000.0000.0001.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.5170.0000.0000.0000.0000.0000.0000.000
CWSU-11 - Issues (Truck Water) - Salinity0.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0290.0000.0000.0000.0000.0000.0001.0000.0000.0000.0640.0001.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.0000.0000.4650.0000.0310.0001.0000.1310.0720.3680.1660.2570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0960.0000.0000.1320.0000.0000.0330.0000.0000.0330.0000.0001.0000.0000.0000.0000.0000.0000.1380.0000.0001.0000.0000.0310.0000.0440.0000.0000.0330.0940.0000.0000.0000.0000.0000.4150.0000.0000.0000.000
CWSU-11 - Issues (Truck Water) - Smell0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1370.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0910.0000.0000.0000.1311.0000.0000.4270.1950.3040.0000.0000.0790.0000.0000.0000.0000.0270.1100.0000.0750.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.0000.0000.1140.0000.0000.0001.0000.0000.0000.0000.0620.1880.0800.0520.3800.0510.0000.0000.0000.0000.4150.0000.0000.0000.000
CWSU-11 - Issues (Truck Water) - Supply interruption0.0000.0000.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.0000.0000.0280.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0780.0000.0000.0000.0360.0000.0000.0000.0370.0330.0240.0720.0720.0001.0000.0000.0000.3540.0000.0000.0000.0000.0000.0000.0170.0000.0000.0550.0000.0000.0000.0000.0000.0690.0000.0000.0750.0000.0000.0000.0000.0000.1190.0000.0001.0000.0000.0000.0000.0000.0000.0000.0500.0001.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0001.0000.2880.1700.1060.000
CWSU-11 - Issues (Truck Water) - Taste0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0001.0000.0000.0000.0640.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2730.0370.0310.0000.3680.4270.0001.0000.1660.2570.0000.0000.0460.0000.0000.0000.0000.1520.0850.0000.0570.0000.0000.0260.0000.0960.0000.0000.1320.0000.0000.0000.0000.0000.0000.0000.0001.0000.1380.1960.0330.0000.0000.1380.0000.0001.0000.0000.0000.0000.0440.1570.0560.0330.3270.0180.0000.0000.0000.0000.4150.0000.0000.0000.000
CWSU-11 - Issues (Truck Water) - Turbidity0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0930.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0940.0000.0000.0000.0000.0000.0000.0000.1060.0000.0000.0000.0000.0000.0000.0000.0000.1190.0000.0000.1660.1660.1950.0000.1661.0000.2190.0000.0000.0440.1190.0000.0000.1010.0000.1480.0140.0000.0000.0000.0000.0000.0370.0000.0000.1140.0000.0000.0000.0000.0000.0001.0001.0001.0000.0000.1920.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0880.0570.0000.0770.1460.0000.0490.9600.1080.0000.2411.0001.0000.0001.000
CWSU-11 - Issues (Truck Water) - Yes0.0000.0000.0340.0000.0000.0000.0160.0000.0240.0580.0000.0080.0280.0000.0000.1350.0000.0000.0000.0000.0820.0001.0000.0000.0000.0000.0000.8040.2580.0000.0000.0000.0000.0000.0000.0000.1600.1520.0000.0000.0000.0000.0000.0000.0000.4400.1720.1640.2570.2570.3040.3540.2570.2191.0000.0630.0000.0200.0000.0000.0000.0000.0000.1300.1170.0710.0000.1120.0000.0000.1370.0000.0000.3030.0000.0000.0000.0530.0360.0300.0000.0000.2420.0000.0000.1030.0000.0000.1170.0660.0000.9290.0000.1210.0000.0000.0000.1810.0000.1210.1210.0000.2570.0000.1980.0000.0000.0000.0000.000
CWSU-12 - Issues (Bottled Water) - Costs0.0000.0280.0000.0000.0000.0000.0000.0000.0470.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.8040.0000.0390.0000.0000.0000.0000.0000.0940.0190.0000.0000.2180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0631.0000.0000.1010.0000.0940.0000.0000.0000.2930.0960.0000.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4690.0001.0000.2050.3330.0270.0000.0000.0590.0000.0000.0000.0000.0230.0000.0000.0000.0930.0000.0000.0500.0000.0000.0690.0000.0000.0000.0000.0000.183
CWSU-12 - Issues (Bottled Water) - N/A0.0350.0000.0000.0000.0250.0000.0000.1020.0190.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0940.0000.0000.0000.0000.0000.0000.0320.0000.2170.0000.0000.0550.0000.0300.0000.0000.1850.1320.0890.0340.0250.0000.0000.0000.0730.0000.2270.1730.0000.0000.0000.0000.0000.0000.0000.0001.0000.5310.0000.0000.0000.0000.0290.0540.0000.0000.0000.0130.0630.0940.0480.0000.0000.0000.1720.0940.0700.0000.0000.1660.1430.0000.3170.1770.0000.0000.0000.0000.0000.0000.0000.3680.1760.0610.0000.0000.0000.0000.0000.0000.0000.0000.2900.1310.2960.0000.0000.1320.0000.192
CWSU-12 - Issues (Bottled Water) - No0.0000.0780.0790.0300.0000.0000.0510.1340.0920.0000.0950.0000.0000.0000.0480.0000.0000.0810.0450.1500.0000.0000.3660.0000.0000.0590.1710.4160.3240.0960.0440.1520.0000.0000.0000.0440.0710.3240.0510.1180.0420.0000.0170.0000.1670.0000.1520.2590.0460.0000.0790.0000.0460.0440.0200.1010.5311.0000.0780.0440.0790.1860.2900.4490.0000.0400.0000.3330.1570.1170.1870.0000.0050.0000.2380.0000.0000.0500.0650.2060.1920.0000.0000.1530.1600.1050.0000.0000.0440.0000.0000.1950.0000.2680.0000.0000.0000.0670.0000.0760.0510.0000.3210.1190.2980.1810.0000.0280.1240.293
CWSU-12 - Issues (Bottled Water) - Others0.0530.0280.0000.0000.0000.0170.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0850.0000.0001.0000.0000.0000.0000.0001.0000.2050.0000.0000.0000.0000.0000.0000.0000.1200.0780.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0720.0000.0000.0000.0000.1190.0000.0000.0000.0781.0000.0000.0000.0170.0000.1760.0000.0000.0000.0090.0680.0000.0080.0370.0000.0750.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.9290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.4200.0000.3380.0000.0000.0000.0000.188
CWSU-12 - Issues (Bottled Water) - Salinity0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.0000.0000.0410.0000.0000.0000.0000.2430.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0440.0001.0000.0000.1010.0000.1480.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0290.0000.7721.0001.0000.4700.6850.0000.0000.0000.0450.0000.0001.0000.0000.0000.0000.0000.0000.1160.0000.0000.0860.0000.0000.0000.0001.0000.0000.0000.0000.399
CWSU-12 - Issues (Bottled Water) - Smell0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.0000.0680.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0001.0000.0750.3200.2080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0360.0000.0000.0001.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.2130.0001.0000.0000.0000.0000.687
CWSU-12 - Issues (Bottled Water) - Supply interruption0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0720.0000.0000.0570.1450.0000.0000.1090.0000.0000.0000.0570.0000.0000.0170.0000.1010.0000.0000.0000.1860.0170.1010.0751.0000.0790.3130.0320.0000.0750.0000.0490.0000.0710.0000.0000.0000.0400.0000.0000.0000.0000.0000.1671.0000.0000.0000.0000.0000.0000.0440.0000.0250.0001.0000.0000.0550.0000.0000.0000.2020.0000.0000.1920.0000.2570.1350.0000.3170.1060.0000.0000.000
CWSU-12 - Issues (Bottled Water) - Taste0.0000.0650.0000.0820.0820.0000.0600.0230.0000.0000.0000.0380.0000.0000.0570.0000.1700.0000.0000.0000.0000.0000.4440.0000.0000.0000.0000.8040.4260.0830.0290.0430.0000.1380.0000.0000.0440.0630.0000.0000.0590.0000.2700.2180.1380.0000.0000.0300.0000.0000.0270.0000.1520.0000.0000.0000.0290.2900.0000.0000.3200.0791.0000.5680.0000.0000.0000.0690.0530.0940.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0860.0000.0000.0920.0000.1240.0000.0000.0240.4790.1650.4030.1630.2210.0000.0000.333
CWSU-12 - Issues (Bottled Water) - Yes0.0000.0980.0000.0000.0400.0000.0000.0000.0060.0180.0520.0930.0380.0000.0290.0410.1120.0000.0000.0000.0000.0000.4440.0000.0000.0000.0000.8040.3210.0690.0000.0000.0000.0790.0000.0000.0000.1650.0000.1730.0190.0000.1830.1170.1970.0360.0000.0360.0000.0000.1100.0000.0850.1480.1300.2930.0540.4490.1760.1480.2080.3130.5681.0000.0000.0860.0530.1540.0810.1220.0000.0000.0000.0360.0630.0000.0000.0000.0000.0000.2280.0000.0000.0180.1360.0620.0480.0620.0000.0000.0000.0000.0000.1390.0000.0000.0650.1440.0480.0660.1160.0870.3770.1700.3570.2260.1220.0000.0000.391
CWSU-13 - Interruptions (days) - Once a week0.0000.0000.0000.0850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0720.0000.0000.0000.0000.0000.0000.0000.0530.0000.1090.0000.0000.0390.0000.0000.0000.0000.0000.1030.0000.0870.1740.1340.0000.0000.1390.0000.0270.0000.1030.0000.0000.0550.0000.0140.1170.0960.0000.0000.0000.0000.0000.0320.0000.0001.0000.0000.0000.2810.0220.0850.0910.0000.1210.0000.0000.0000.0000.0000.0000.0000.2030.0140.1690.0000.0000.0000.0000.0630.0000.0000.0000.0000.0800.0960.0000.1460.0350.3370.0000.0000.3140.0200.1320.1220.1710.1260.1350.0000.0000.000
CWSU-13 - Interruptions (days) - Thrice a week0.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0001.0000.0000.0000.0000.0000.8040.0570.0310.0770.0000.0000.0000.0000.0000.0000.0720.0000.0000.0000.0860.0600.0000.1090.0000.0000.0000.0000.0000.0750.0000.0570.0000.0710.0000.0000.0400.0000.0000.0000.0000.0000.0860.0001.0000.0000.1260.0000.0000.0000.0170.1120.0000.0000.0000.1110.0000.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0790.0000.0000.0550.0000.0000.0000.1540.0000.0430.1090.0000.0000.1270.1950.0000.0000.0000.0000.193
CWSU-13 - Interruptions (days) - Twice a week0.1620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0210.0000.0000.0001.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.1210.0980.0000.1080.0000.0000.0920.1290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0530.0000.0001.0000.2100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.3450.0000.2680.2190.0000.0000.1040.0000.0000.0000.5870.0420.0000.0000.0000.0870.0000.1190.0000.1290.0000.0000.1950.2130.0000.0000.0000.0000.000
CWSU-13 - Interruptions (days) - We have continuous water supply everyday0.0000.1180.1010.0320.0000.0160.0000.0830.0000.0000.0000.0610.0000.0000.0540.0000.1570.0560.0000.0000.2090.0000.8370.0470.0000.0000.1850.3970.5510.0000.0430.0670.0000.0650.0000.0000.0000.3150.0600.0530.1640.1080.1570.1210.2150.0000.0240.1080.0620.0000.0390.0000.0000.0000.1120.0730.0130.3330.0090.0000.0000.0000.0690.1540.2810.1260.2101.0000.1380.0000.1370.0000.0250.0290.1020.0000.0000.0000.0000.1990.0630.1780.1350.2240.2500.2410.0000.0000.0000.0000.0000.1740.0630.2680.0220.1160.1070.1270.0490.0790.1820.0000.5400.2560.5460.0000.0000.0000.2330.114
CWSU-2 - Treatment0.0310.0000.0000.0000.0500.0000.0000.0000.1570.0680.0940.0000.0600.0780.0710.1110.0900.1960.0000.0000.0000.1450.0000.0660.0000.0000.0000.4140.5420.0000.0580.1150.0670.0000.0000.0000.0000.0590.0000.0000.0000.0550.0000.0000.0000.0140.0830.0000.0000.0000.0000.0000.0260.0000.0000.0000.0630.1570.0680.0000.0000.0490.0530.0810.0220.0000.0000.1381.0000.0780.1430.0000.0710.0420.0000.2200.0000.1060.0000.2570.1080.0000.0000.1550.0340.0750.0000.0710.0780.0250.0000.0000.0000.0000.0000.0000.0000.0680.0340.0440.0160.1050.4660.2180.4960.1860.1870.1790.2620.133
CWSU-3 - Current Sources - Bottled water (5 gallons)0.0000.0280.0310.0460.0930.0000.1080.0240.0620.0000.0560.1510.0170.0500.0860.0000.0920.0810.0000.0000.0000.0000.0000.0000.0000.0000.1790.0000.3360.0590.2370.1890.0000.0680.0000.0000.0000.0000.0360.0000.0000.0000.0470.1000.0340.0000.0820.0460.0000.0000.0000.0000.0000.0000.0000.0000.0940.1170.0000.0000.0000.0000.0940.1220.0850.0000.0000.0000.0781.0000.0000.0000.0700.0000.3530.0460.0000.0000.0000.0800.0000.1250.1250.0320.1130.0580.0600.1300.0000.0000.0720.0550.0490.0170.0000.0000.0000.0180.0000.0000.0000.0000.2270.0220.3500.0000.0380.0920.1430.158
CWSU-3 - Current Sources - Deep Well (owned)0.0000.0000.0000.0580.0450.0000.1360.1150.0570.0000.0000.0000.0000.0000.0930.0000.0000.0330.0000.0000.0000.0220.0000.0610.0000.0000.1530.0000.2620.1730.1010.2140.1030.0000.0000.0370.3230.3700.0000.0390.0000.1280.0470.0830.0940.1100.0440.0500.0370.0960.0000.0690.0960.0370.1370.0000.0480.1870.0080.0370.0000.0710.0000.0000.0910.0000.0000.1370.1430.0001.0000.1100.2990.3150.1310.2020.0000.0000.1520.1700.0890.3220.0050.2540.1610.1760.0070.0590.1040.0000.0000.5250.1170.0000.0000.0000.0000.1180.0000.0680.1140.1720.1850.1380.2580.1410.0860.1150.2640.224
CWSU-3 - Current Sources - Others0.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.1101.0000.0000.0000.0000.0000.3520.0000.0000.0000.0001.0001.0000.2510.2570.0000.0000.0000.0000.0000.0000.9290.0230.0000.0000.0000.0000.0000.0000.0000.0390.0330.0000.0000.2961.0000.0000.0000.0630.000
CWSU-3 - Current Sources - Tap Water (Water District etc.)0.0000.0310.1060.0220.0000.0000.0000.0640.0430.0000.0000.0000.0000.0000.0110.0000.0140.0370.0000.0000.0000.0000.0000.0000.0000.0000.0540.0910.2050.0880.0000.0000.0000.0000.0000.0000.1140.0510.0400.0000.0000.0000.0000.0790.0540.0000.0450.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.1210.1120.0000.0250.0710.0700.2990.0001.0000.0230.0370.0630.0000.3390.0000.1160.0000.0000.0000.0000.1000.0360.0100.2060.0310.0000.0000.0510.2500.1650.0000.0000.0000.2260.0000.0450.2130.1210.0000.1670.1830.0000.1830.1380.1490.000
CWSU-3 - Current Sources - Truck Delivery Services (5 m3)0.1500.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.1090.0000.0080.0000.0000.0000.0000.0000.1950.0000.1320.0930.2420.3850.0380.0270.0000.0390.0820.0000.0000.2210.2030.0000.0000.0000.0000.0520.0000.0000.2400.1450.0220.0540.1320.0000.0750.1320.1140.3030.0000.0000.0000.0750.0000.0000.0000.0000.0360.0000.0000.0000.0290.0420.0000.3150.0000.0231.0000.0000.1050.0000.0000.3760.1590.1100.0000.5880.0980.1200.1740.0000.0000.2410.0320.1620.8650.0000.1410.0000.0210.0000.0470.0000.0000.0710.0000.2390.1040.3490.2190.1540.0000.2030.266
CWSU-4 - Primary Drinking Source - Bottled water (5 gallons)0.0000.0320.0000.1170.0440.0000.1200.1940.0200.0340.0000.1660.0000.0000.0000.0910.0850.0710.0140.0000.0000.0000.0000.0000.0000.0000.1860.2060.3180.1610.1080.1380.0000.0000.0000.0590.0000.0430.0460.0000.0000.0000.0000.0830.0040.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.1720.2380.0000.0000.0000.0400.0280.0630.0000.0000.0000.1020.0000.3530.1310.0000.0370.0001.0000.0000.2080.0510.2920.3030.0570.0000.2480.1660.1520.1100.0000.0430.0150.0000.0000.0000.0450.0680.0000.0000.0000.0360.0000.0000.0000.1590.3000.2350.2290.1270.0000.0000.1980.266
CWSU-4 - Primary Drinking Source - Deep Well (owned)0.0000.0000.0000.0810.1890.0000.1450.0600.0970.0630.0290.0000.0000.0200.0000.0390.0000.0840.0620.0000.0000.0001.0000.0000.0000.0000.0190.0000.2640.0320.1380.1160.0300.0000.0000.0000.1160.0860.0000.0000.0000.0200.0000.0000.0550.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2200.0460.2020.0000.0630.1050.0001.0000.0000.1150.0310.1290.0800.0000.0000.2330.0000.0000.0000.0000.0000.0000.0000.0000.0870.0340.0000.0000.0000.0310.0000.0000.0000.0000.1850.1530.2190.1800.0840.0000.0660.123
CWSU-4 - Primary Drinking Source - Others0.0000.0000.0000.0000.0000.0000.0000.0000.0360.0780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0001.0000.0000.0000.0000.0240.0000.0730.0000.0850.0720.0000.0000.0520.0000.0470.0310.0000.0000.0000.0000.0000.0000.0000.0000.0830.0250.0000.0330.0000.0000.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.1110.0000.0000.0000.0000.0000.3520.0000.0000.2080.0001.0000.0000.0000.0760.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.9290.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
CWSU-4 - Primary Drinking Source - Tap Water (Water District etc.)0.0000.0000.0940.0000.0760.0000.0400.0520.0000.0000.0000.0000.0000.0000.0630.0600.0000.0810.0490.0000.0000.0000.0000.0000.0000.0000.0000.4780.2050.0000.0000.0540.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1060.0000.0000.0000.3390.0000.0510.1150.0001.0000.0000.0440.0000.2190.0770.0000.0000.0000.0000.1340.0000.0000.0000.0000.0820.0000.0000.0000.0000.1040.0000.0000.0840.0000.1310.0000.2560.0000.1450.0680.0000.059
CWSU-4 - Primary Drinking Source - Truck Delivery Services (5 m3)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1040.0000.0000.0000.0000.0000.0000.0000.0190.0001.0000.0480.0000.0000.0000.8040.2340.0000.0380.0260.0000.0810.0000.1860.0540.0740.0000.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0650.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1520.0000.0000.3760.2920.0310.0000.0001.0000.0410.1950.1430.0000.4070.2630.0450.0000.0000.0850.0000.0000.4920.0000.0630.0000.1020.0000.1330.1450.0000.1480.0000.3430.1400.2230.2200.0320.0000.0000.174
CWSU-4 - Primary Drinking Source - gals0.0000.2260.1130.1760.1790.0240.1760.2260.1130.0930.0000.1580.1300.0000.1530.0180.2080.1490.0410.2500.0930.1431.0000.0000.0000.0000.1680.0000.4140.2340.2130.1700.2030.2710.0000.0000.0000.0900.0000.0000.0360.1610.0000.0710.0000.0000.0530.1170.0000.0330.0000.1190.0000.0000.0300.0000.1660.2060.0000.0000.0000.0000.0000.0000.0000.1030.0840.1990.2570.0800.1700.0000.1160.1590.3030.1290.0760.0440.0411.000-0.0550.407-0.0590.0970.0550.2680.0000.1520.1730.1180.1300.6450.1350.1430.0000.1170.0000.1510.0910.0000.1000.4440.3400.2000.305-0.0130.1950.0430.2450.020
CWSU-5 - Ave Demand (in cbm)0.0000.0890.0000.0000.0000.0000.0490.0000.0760.0870.0000.0000.0910.0000.0580.2970.0000.0000.0000.0000.0000.0000.0000.0000.3700.2590.1761.0000.1470.0000.1200.0000.4020.0000.0000.9880.0840.0000.0000.4120.1170.0000.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.4690.1430.1920.0000.7720.0000.1670.0000.2280.2030.0000.0000.0630.1080.0000.0890.0000.0000.1100.0570.0800.0000.0000.195-0.0551.0000.591-0.4180.3720.4070.0000.0000.0800.4410.0000.0881.0000.0600.0000.0000.0000.0000.1820.0000.0000.1321.0000.3230.0000.000NaN0.1320.0000.3040.355
CWSU-6 - Actual Use (m3 / gal) - Deep Well (owned)0.0000.0000.0000.0000.0420.0570.0000.0000.1630.1580.2280.1050.0000.0000.0000.0000.2840.0000.0001.0001.0001.0001.0000.4500.0570.2880.3290.2970.0000.0000.3800.2140.0480.0000.0001.0000.0000.1010.0000.0000.3360.0000.0860.1640.0490.0000.1080.0001.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0001.0001.0001.0000.0000.0000.0140.0000.3450.1780.0000.1250.3221.0000.0000.0000.0000.0000.0000.2190.1430.4070.5911.0000.2730.0000.0000.2780.0000.1420.4130.0000.3671.0000.1550.0991.0000.0000.0000.0000.0000.0000.0000.1880.0000.0000.0000.2980.1010.0000.3440.360
CWSU-6 - Actual Use (m3 / gal) - Tap Water (Water District etc.)0.5700.0000.0000.1210.0001.0000.0000.3170.0050.0050.0170.0000.1561.0000.0000.0000.0000.0000.0001.0001.0001.000NaN0.0000.0000.5720.0001.0000.0000.2610.2920.0000.3970.0001.0001.0000.5520.1680.9220.7620.5720.2740.0001.0000.2171.0000.1210.0001.0001.0001.0001.0001.0001.0000.2421.0000.3170.0001.0001.0001.0000.0001.0000.0000.1690.0000.0000.1350.0000.1250.0051.0000.0000.5880.2480.0001.0000.0770.000-0.059-0.4180.2731.0000.0000.0490.1931.0000.0000.3120.0000.000NaN1.0000.2731.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.0000.4990.3310.000
CWSU-7 - Peak Month - End0.2910.1610.2330.1960.1850.0000.1610.1170.0920.0720.1500.1160.0710.0000.2040.1600.0000.0000.0890.1010.0000.0000.0000.0140.0000.0630.1360.2170.2650.1620.0000.1430.0000.1060.0000.6820.1280.2340.2410.2480.0990.2100.0000.0000.1310.0000.2060.1760.0000.0001.0000.0000.1380.0000.0000.2050.1770.1530.0000.4700.0000.0000.0000.0180.0000.0000.2680.2240.1550.0320.2540.2510.0000.0980.1660.2330.0000.0000.4070.0970.3720.0000.0001.0000.4590.1000.0000.0000.2360.1180.0000.3570.0000.0000.1880.1790.0860.0000.0000.1530.0000.1520.1820.0700.2730.0000.0000.0000.1250.133
CWSU-7 - Peak Month - Start0.3460.1080.0000.0990.0820.0000.0000.0000.1570.1500.0000.0000.1590.0880.1460.1990.0000.0990.0000.1200.3470.0000.5770.1530.0000.2880.0000.3230.2870.0000.0640.0990.0000.0770.0000.9830.0780.1310.1340.4190.1550.0000.0510.0000.0000.0000.1450.2040.0640.0001.0000.0000.1960.1920.0000.3330.0000.1600.0000.6850.0000.0000.0000.1360.0000.0000.2190.2500.0340.1130.1610.2570.1000.1200.1520.0000.0000.0000.2630.0550.4070.0000.0490.4591.0000.0260.0000.0990.2540.0000.0000.4630.1540.1140.2880.0000.1610.0000.1630.1380.0000.2810.3720.1070.2930.0000.0000.0970.0910.121
CWSU-8 - Monthly Costs - Less than PhP 1,0000.0500.0000.0330.0000.0290.0570.0650.0000.0000.0000.0000.0000.0000.0000.0000.0000.1010.0340.0530.1220.0400.0000.5410.1570.0680.0840.3270.2600.4200.0520.0650.0000.0250.1430.0000.0000.0000.1050.0000.0000.0520.0150.0000.0450.0000.0740.0970.0000.0000.0000.0000.0000.0330.0000.1030.0270.0000.1050.0000.0000.0000.0000.0640.0620.0000.0000.0000.2410.0750.0580.1760.0000.0360.1740.1100.0000.0000.0000.0450.2680.0000.2780.1930.1000.0261.0000.0000.4220.1720.2270.1570.2880.1140.0180.0000.0000.0390.0220.0000.0000.0000.2480.3950.2440.3520.1630.1200.0970.2900.000
CWSU-8 - Monthly Costs - Others:0.0000.0150.0000.0000.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0341.0001.0001.0001.0000.0000.0000.0000.0000.0000.0350.0000.0000.0370.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0820.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0600.0070.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.5910.0000.0250.0000.0000.0000.0000.0000.0000.0000.0760.2160.0000.0001.0000.0000.0000.0790.177
CWSU-8 - Monthly Costs - PhP 1,000 - PhP 3,0000.0000.0980.0000.0370.0000.0000.0000.0590.0000.0270.0000.0000.0000.1140.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.0000.2500.0420.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0730.0500.0000.0000.1140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0440.0000.0620.0630.0000.1040.0000.0710.1300.0590.0000.2060.0000.0430.0000.0000.1340.0000.1520.0800.1420.0000.0000.0990.4220.0001.0000.0850.1200.0750.4180.0570.0000.0000.0000.0220.0910.0380.1160.0830.0990.1690.1950.1560.2240.1820.1390.2230.000
CWSU-8 - Monthly Costs - PhP 10,000 and above0.0630.0000.0260.0610.0080.0000.0830.0000.0380.0690.0220.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0001.0000.0000.0000.3980.1500.8040.0540.0350.0000.0370.0620.0000.0000.0450.0580.0500.0000.0510.0000.0000.0000.0000.0000.0880.0000.0330.0000.1380.0000.0000.1380.0000.1170.0590.0000.0440.0000.0450.0000.0000.0000.0000.0000.0680.0000.0000.0780.0000.1040.0000.0310.2410.0150.0000.0000.0000.0850.1730.4410.4130.3120.2360.2540.1720.0000.0851.0000.0000.0000.9290.0810.0000.0000.0000.0000.0760.0000.0000.0000.0910.0000.0000.0430.2110.0000.0000.4070.354
CWSU-8 - Monthly Costs - PhP 3,000 - PhP 5,0000.0000.0000.0800.0000.0730.0000.0000.0250.0000.0000.0000.0160.0000.0000.0000.0470.0000.0000.0000.0000.0460.0001.0000.2490.0000.0000.0000.0000.1470.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0660.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.1180.0000.0000.0000.1180.0000.2270.0000.1200.0001.0000.0001.0000.0660.0370.0000.0000.0000.0000.0640.0000.0000.0810.2220.0540.0000.0000.0000.0000.2630.113
CWSU-8 - Monthly Costs - PhP 5,000 - PhP 10,0000.0000.0000.0000.0000.0190.0790.0000.0000.0560.0230.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0001.0000.0860.2290.0000.0840.8040.0000.0000.0700.0550.0000.0000.0000.0000.0000.0630.0000.0000.0000.1830.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.0720.0000.0000.0000.1620.0000.0000.0000.0000.0000.1300.0880.3670.0000.0000.0000.1570.0000.0750.0000.0001.0000.9290.0000.0610.0000.0630.0000.0000.0000.0000.0000.0770.1530.0000.0560.0000.2370.1230.3120.000
CWSU-8 - Monthly Costs - Truck Delivery0.9290.8540.5910.1180.0000.9290.2280.0590.0000.0000.0000.0000.3150.0000.0000.3900.1330.0000.1000.3250.0000.0001.0000.5910.5910.9290.3380.5000.6710.0320.0000.4370.0000.0001.0000.0000.4780.1930.7190.0000.0000.0000.0000.0000.0000.9290.2380.1811.0001.0001.0001.0001.0001.0000.9290.0000.3680.1950.9291.0001.0001.0000.0000.0000.0000.0000.5870.1740.0000.0550.5250.9290.0510.8650.0000.0000.9290.0000.4920.6451.0001.000NaN0.3570.4630.2880.5910.4180.9291.0000.9291.0000.1150.0000.5911.0000.0000.4260.0001.0000.3230.9290.4830.5680.4240.0000.0000.7070.8620.866
CWSU-9 - Issues (Tap Water) - N/A0.0000.0130.1200.0450.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.1220.0000.0000.0000.0350.0000.0350.0390.0000.4770.0000.0000.0000.0000.0720.0000.0000.1600.0250.0000.0000.0000.0000.0000.0310.0650.0000.3610.2630.0000.0000.0000.0000.0000.0000.0000.0000.1760.0000.0000.0000.0000.0000.0000.0000.0800.0000.0420.0630.0000.0490.1170.0230.2500.0000.0450.0870.0000.0820.0000.1350.0600.1551.0000.0000.1540.1140.0000.0570.0810.0660.0000.1151.0000.4970.0000.0570.0910.2030.0680.0000.2580.2270.3940.2710.4270.0000.1410.1190.2270.068
CWSU-9 - Issues (Tap Water) - No0.0480.0000.0790.0000.0510.0000.0000.0000.0000.0240.0750.0000.0000.0000.1250.0000.0980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.4020.0630.0630.1110.0000.0310.0000.0000.0370.3450.0610.1060.1240.1700.1580.1190.2750.0240.2660.4120.0310.0310.0000.0240.0000.0000.1210.0230.0610.2680.0000.0000.0000.0550.0860.1390.0960.0550.0000.2680.0000.0170.0000.0000.1650.1410.0680.0340.0250.0000.0630.1430.0000.0990.2730.0000.1140.0180.0250.0000.0000.0370.0610.0000.4971.0000.0480.1060.1480.3050.1190.0480.3730.0000.3800.1620.4010.0320.0520.0760.1090.220
CWSU-9 - Issues (Tap Water) - Others0.0000.0000.0000.0100.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0850.1440.0001.0000.0000.0000.0000.0810.0000.2520.0000.0000.0000.0000.0000.0000.0000.0000.0000.1460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.1880.2880.0000.0000.0000.0000.0000.0000.5910.0000.0481.0000.0000.1350.0000.0130.0000.0750.0830.5230.1740.0000.0000.0000.0000.0000.000
CWSU-9 - Issues (Tap Water) - Salinity0.0000.0000.0000.0000.0000.0000.0000.0000.0920.1100.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0001.0000.0000.0000.0000.0001.0000.0430.0000.0430.0000.0000.0000.0000.0000.0000.1000.0000.2050.0000.2240.0430.0000.0830.0000.0000.0000.0000.0440.0620.0000.0440.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1460.0000.0000.1160.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.1020.1170.0000.0000.0000.1790.0000.0000.0000.0000.0000.0000.0631.0000.0570.1060.0001.0000.0670.1720.2720.1850.3080.0000.0000.0000.0590.4150.0000.0000.0000.000
CWSU-9 - Issues (Tap Water) - Smell0.0000.0000.0000.0470.0000.0000.0000.0000.0500.0750.0710.0650.0000.1350.0000.0640.0000.0150.1300.1310.0000.0001.0000.0000.0000.0000.0001.0000.2460.0000.0000.0000.0000.0000.0000.0000.0480.1700.0000.0670.3720.0000.2800.1030.2060.0000.0000.0000.0000.0000.1880.0000.1570.0570.0000.0000.0000.0000.0000.0000.0310.0000.0920.0650.0350.0000.0870.1070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0860.1610.0390.0000.0220.0000.0000.0000.0000.0910.1480.1350.0671.0000.2420.4050.3560.4080.1230.1500.2120.2100.0000.0000.0000.0120.000
CWSU-9 - Issues (Tap Water) - Supply interruption0.0000.0000.0000.0000.0000.0000.0320.0540.0100.0730.0710.0870.0000.0000.0810.0000.0000.0550.0980.0000.0000.0000.0000.0000.0000.0000.0000.1270.1990.0000.0000.0000.0000.0000.0000.0000.0000.1190.0000.1280.0000.2370.0180.0000.1640.0000.0000.0000.0000.0000.0800.0000.0560.0000.1810.0930.0000.0670.0000.1160.0000.2020.0000.1440.3370.1540.0000.1270.0680.0180.1180.0000.2260.0470.0360.0310.0000.1040.1330.1510.1820.0000.0000.0000.0000.0220.0000.0910.0760.0000.0000.4260.2030.3050.0000.1720.2421.0000.1480.0350.7610.1010.1380.2380.1510.1860.0000.0000.0000.220
CWSU-9 - Issues (Tap Water) - Taste0.0000.0000.0000.0000.0190.0000.0000.0000.0820.1020.0000.0000.0000.0350.0000.0000.0000.0000.0360.0000.0190.0001.0000.0490.0000.0000.0001.0000.1800.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0940.1580.0000.2830.1360.0990.0000.0000.0000.0000.0330.0520.0000.0330.0770.0000.0000.0000.0000.0000.0000.0000.0000.1240.0480.0000.0000.1190.0490.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.1450.0910.0000.0000.0000.0000.1630.0000.0000.0380.0000.0640.0000.0000.0680.1190.0130.2720.4050.1481.0000.1670.3060.0660.0000.1750.2440.0000.0000.0000.1120.000
CWSU-9 - Issues (Tap Water) - Turbidity0.0000.0300.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0890.0000.0290.1060.0000.1100.0000.0000.0580.0000.0480.0000.0940.3800.0000.3270.1460.1210.0000.0000.0760.0000.0000.0000.0000.0000.0660.0000.0430.0000.0790.0440.0000.0680.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1530.1380.0000.0000.1160.0000.0000.0001.0000.0000.0480.0000.1850.3560.0350.1671.0000.1890.0000.0000.1650.2890.2320.0000.0000.0000.000
CWSU-9 - Issues (Tap Water) - Yes0.0000.0000.0250.0270.0000.0000.0000.0220.0920.1340.0790.0990.0000.0540.0540.0000.0000.0000.1170.0000.0000.0000.0000.0000.0000.0000.0000.0000.2080.0000.0110.0000.0000.0470.0000.0000.0000.2100.0280.1230.1180.2560.1260.0000.2450.0000.0000.0460.0000.0000.0510.0000.0180.0000.1210.0500.0000.0510.0000.0860.0000.1920.0000.1160.3140.1090.1290.1820.0160.0000.1140.0390.2130.0710.0000.0000.0000.0840.1480.1000.1320.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.3230.2580.3730.0750.3080.4080.7610.3060.1891.0000.0640.1580.1500.1840.0000.0000.0000.0000.000
Classification0.0550.4140.1010.0000.0000.0000.0390.0700.0820.1170.0000.0000.0000.0000.0000.0780.0000.0000.0401.0001.0001.0001.0000.1030.0580.0430.1020.6450.8100.0000.1740.2420.0290.1220.0000.0000.0000.0000.0620.0000.1250.0000.1340.0500.1020.0000.1650.0280.0000.0000.0000.0330.0000.0490.0000.0000.0000.0000.0380.0000.0000.0000.0240.0870.0200.0000.0000.0000.1050.0000.1720.0330.1210.0000.1590.0000.0000.0000.0000.4441.0000.1880.0000.1520.2810.2480.0760.0990.0910.0810.0770.9290.2270.0000.0830.0000.1230.1010.0660.0000.0641.0000.5330.8560.5181.0001.0001.0000.9901.000
GI - Business Location/Address0.1900.4400.1310.4820.4850.0000.3040.3620.2900.2760.2090.2720.2900.0000.3410.3190.7880.3690.2710.3680.4380.1000.3670.0760.4840.0000.3080.5360.8180.5050.3610.2560.3310.4490.0000.0000.3240.3470.1820.3060.2760.2870.4900.6720.3160.0000.5250.5080.5170.0000.0000.0000.0000.9600.2570.0000.2900.3210.4200.0000.0000.2570.4790.3770.1320.0000.0000.5400.4660.2270.1850.0000.0000.2390.3000.1850.0000.1310.3430.3400.3230.0000.0000.1820.3720.3950.2160.1690.0000.2220.1530.4830.3940.3800.5230.0000.1500.1380.0000.0000.1580.5331.0000.6190.6630.0000.0000.0000.3750.290
GI - Date0.1160.3790.2010.1890.1670.0000.1260.1430.0000.0680.0000.1020.0920.0000.2250.2070.1730.1750.1480.0760.0700.2070.3770.0000.0890.0000.2270.4100.5140.2170.1990.2050.0460.2110.0000.0760.1160.1550.1790.1450.2040.0000.1840.0730.1330.0230.1270.0000.0000.0000.0000.0000.0000.1080.0000.0690.1310.1190.0000.0000.2130.1350.1650.1700.1220.1270.1950.2560.2180.0220.1380.0000.1670.1040.2350.1530.0000.0000.1400.2000.0000.0000.0000.0700.1070.2440.0000.1950.0000.0540.0000.5680.2710.1620.1740.0000.2120.2380.1750.1650.1500.8560.6191.0000.3850.1630.0000.0000.3200.283
GI - Interviewer0.2580.3610.0850.4300.4740.1740.2940.2140.3260.3220.2920.2900.3700.2270.4090.3570.7920.3540.2730.0000.5090.1800.4080.0000.0000.0000.3540.2270.7740.5480.3110.2520.1550.3090.0000.0000.3450.2700.0720.3040.1800.0000.4060.3640.1090.0000.5040.5050.0000.0000.0000.0000.0000.0000.1980.0000.2960.2980.3380.0000.0000.0000.4030.3570.1710.1950.2130.5460.4960.3500.2580.2960.1830.3490.2290.2190.0000.2560.2230.3050.0000.0000.0000.2730.2930.3520.0000.1560.0430.0000.0560.4240.4270.4010.0000.0590.2100.1510.2440.2890.1840.5180.6630.3851.0000.0000.0320.1070.2920.226
GI - Members0.0000.1700.0000.0000.1830.3110.0000.0000.3040.2650.0000.0950.0000.0000.1300.0000.0000.0000.0990.0750.0120.0000.0001.0001.0001.0000.0000.0000.0790.0000.0000.0690.0000.1671.0001.0000.0000.1180.0000.0000.0000.0000.0000.0000.0000.2190.0000.0000.0000.4150.4151.0000.4150.2410.0000.0000.0000.1810.0001.0001.0000.3170.1630.2260.1260.0000.0000.0000.1860.0000.1411.0000.0000.2190.1270.1800.0000.0000.220-0.013NaN0.298NaN0.0000.0000.1631.0000.2240.2110.0000.0000.0000.0000.0320.0000.4150.0000.1860.0000.2320.0001.0000.0000.1630.0001.0000.0000.0001.0000.000
GI - Operating Hours - End0.0000.1270.0000.0730.0000.0000.0000.0000.0990.0750.0000.0560.0000.1760.0000.1500.0000.4320.0310.0000.0000.0000.0000.0000.0000.1670.0000.0000.1350.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.2680.1030.0000.0000.0000.0000.0000.0000.2880.0001.0000.0000.0000.0000.0000.0000.0000.0000.1060.2210.1220.1350.0000.0000.0000.1870.0380.0860.0000.1830.1540.0000.0840.0000.1450.0320.1950.1320.1010.0000.0000.0000.1200.0000.1820.0000.0000.2370.0000.1410.0520.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0320.0001.0000.6400.0630.000
GI - Operating Hours - Start0.0000.0720.1680.0000.0000.0000.0520.0000.0000.0000.0890.1350.0230.1930.0000.0360.0000.4250.1840.0000.0000.0000.0000.0000.0000.0000.0000.0000.1400.0000.1450.1910.0000.0000.0000.0000.1240.0000.0000.0000.0000.0950.0000.0000.0000.0000.1390.1800.0000.0000.0000.1700.0001.0000.0000.0000.1320.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1790.0920.1150.0000.1380.0000.0000.0000.0000.0680.0000.0430.0000.0000.4990.0000.0970.0970.0000.1390.0000.0000.1230.7070.1190.0760.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.1070.0000.6401.0000.0850.000
GI - Remarks (Small, Medium, Large, Residential)0.0000.4620.1040.2360.2220.0000.0670.1520.1550.1600.0000.0000.1320.0000.0000.1770.0000.1360.0001.0001.0001.0001.0000.1970.3610.3150.2160.5530.4490.0000.2300.4140.3710.1900.2680.0000.0450.1550.0000.0730.2100.1160.0900.2430.1270.0000.2370.1930.0000.0000.0000.1060.0000.0000.0000.0000.0000.1240.0000.0000.0000.0000.0000.0000.0000.0000.0000.2330.2620.1430.2640.0630.1490.2030.1980.0660.0000.0000.0000.2450.3040.3440.3310.1250.0910.2900.0790.2230.4070.2630.3120.8620.2270.1090.0000.0000.0120.0000.1120.0000.0000.9900.3750.3200.2921.0000.0630.0851.0000.427
GI - Type of Business0.2820.2120.1400.1360.1680.0000.1490.2750.2230.2270.0000.0000.2050.0000.0000.2690.1110.0000.0370.0000.0000.0000.0000.0230.4380.0000.1660.5320.2720.2500.3700.5600.4170.0940.0000.6410.1180.1760.0580.7150.0000.0000.0000.1320.0730.0000.0530.0880.0000.0000.0000.0000.0001.0000.0000.1830.1920.2930.1880.3990.6870.0000.3330.3910.0000.1930.0000.1140.1330.1580.2240.0000.0000.2660.2660.1230.0000.0590.1740.0200.3550.3600.0000.1330.1210.0000.1770.0000.3540.1130.0000.8660.0680.2200.0000.0000.0000.2200.0000.0000.0001.0000.2900.2830.2260.0000.0000.0000.4271.000

Missing values

2025-07-05T17:35:49.874586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-05T17:35:54.562024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-05T17:35:56.321900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GI - CodeBarangayClassificationGI - DateGI - InterviewerGI - Remarks (Small, Medium, Large, Residential)GI - Business Name/Residential Name/Contact numberGI - Business Location/AddressGI - Type of BusinessGI - No. of EmployeesGI - Operating Hours - StartGI - Operating Hours - EndCWSU-1 - Usage - SpecifyGI - MembersCWSU-1 - Usage - DrinkingCWSU-1 - Usage - Food preparationCWSU-1 - Usage - Cleaning & SanitationCWSU-1 - Usage - Manufacturing/ProductionCWSU-1 - Usage - Landscaping/IrrigationCWSU-1 - Usage - OthersCWSU-2 - TreatmentCWSU-3 - Current Sources - Tap Water (Water District etc.)CWSU-3 - Current Sources - Deep Well (owned)CWSU-3 - Current Sources - Truck Delivery Services (5 m3)CWSU-3 - Current Sources - Bottled water (5 gallons)CWSU-3 - Current Sources - OthersCWSU-3 - Current Sources - (If others, please specify)CWSU-4 - Primary Drinking Source - Tap Water (Water District etc.)CWSU-4 - Primary Drinking Source - Deep Well (owned)CWSU-4 - Primary Drinking Source - Truck Delivery Services (5 m3)CWSU-4 - Primary Drinking Source - Bottled water (5 gallons)CWSU-4 - Primary Drinking Source - OthersCWSU-4 - Primary Drinking Source - (If others, please specify)CWSU-4 - Primary Drinking Source - galsCWSU-5 - Ave Demand (in cbm)CWSU-6 - Actual Use (m3 / gal) - Tap Water (Water District etc.)CWSU-6 - Actual Use (m3 / gal) - Deep Well (owned)CWSU-6 - Actual Use (m3 / gal) - Truck Delivery Services (5 m3)CWSU-6 - Actual Use (m3 / gal) - Bottled water (5 gallons)CWSU-6 - Actual Use (m3 / gal) - Others (gallons)CWSU-7 - Peak Month - StartCWSU-7 - Peak Month - EndCWSU-8 - Monthly Costs - Less than PhP 1,000CWSU-8 - Monthly Costs - PhP 1,000 - PhP 3,000CWSU-8 - Monthly Costs - PhP 3,000 - PhP 5,000CWSU-8 - Monthly Costs - PhP 5,000 - PhP 10,000CWSU-8 - Monthly Costs - PhP 10,000 and aboveCWSU-8 - Monthly Costs - Water BottlesCWSU-8 - Monthly Costs - Truck DeliveryCWSU-8 - Monthly Costs - Others:CWSU-8 - Monthly Costs - If others, input hereCWSU-9 - Issues (Tap Water) - YesCWSU-9 - Issues (Tap Water) - NoCWSU-9 - Issues (Tap Water) - N/ACWSU-9 - Issues (Tap Water) - Supply interruptionCWSU-9 - Issues (Tap Water) - SalinityCWSU-9 - Issues (Tap Water) - TasteCWSU-9 - Issues (Tap Water) - SmellCWSU-9 - Issues (Tap Water) - TurbidityCWSU-9 - Issues (Tap Water) - CostsCWSU-9 - Issues (Tap Water) - OthersCWSU-9 - Issues (Tap Water) - If others, input hereCWSU-10 - Issues (Deep Well) - YesCWSU-10 - Issues (Deep Well) - NoCWSU-10 - Issues (Deep Well) - N/ACWSU-10 - Issues (Deep Well) - Supply interruptionCWSU-10 - Issues (Deep Well) - SalinityCWSU-10 - Issues (Deep Well) - TasteCWSU-10 - Issues (Deep Well) - SmellCWSU-10 - Issues (Deep Well) - TurbidityCWSU-10 - Issues (Deep Well) - CostsCWSU-10 - Issues (Deep Well) - OthersCWSU-10 - Issues (Deep Well) - If others, input hereCWSU-11 - Issues (Truck Water) - YesCWSU-11 - Issues (Truck Water) - NoCWSU-11 - Issues (Truck Water) - N/ACWSU-11 - Issues (Truck Water) - Supply interruptionCWSU-11 - Issues (Truck Water) - SalinityCWSU-11 - Issues (Truck Water) - TasteCWSU-11 - Issues (Truck Water) - SmellCWSU-11 - Issues (Truck Water) - TurbidityCWSU-11 - Issues (Truck Water) - CostsCWSU-11 - Issues (Truck Water) - OthersCWSU-11 - Issues (Truck Water) - If others, input hereCWSU-12 - Issues (Bottled Water) - YesCWSU-12 - Issues (Bottled Water) - NoCWSU-12 - Issues (Bottled Water) - N/ACWSU-12 - Issues (Bottled Water) - If yes, select all that applyCWSU-12 - Issues (Bottled Water) - Supply interruptionCWSU-12 - Issues (Bottled Water) - SalinityCWSU-12 - Issues (Bottled Water) - TasteCWSU-12 - Issues (Bottled Water) - SmellCWSU-12 - Issues (Bottled Water) - TurbidityCWSU-12 - Issues (Bottled Water) - CostsCWSU-12 - Issues (Bottled Water) - OthersCWSU-12 - Issues (Bottled Water) - If others, input hereCWSU-13 - Interruptions (days) - We have continuous water supply everydayCWSU-13 - Interruptions (days) - Once a weekCWSU-13 - Interruptions (days) - Twice a weekCWSU-13 - Interruptions (days) - Thrice a weekCWSU-13 - Interruptions (days) - OthersCWSU-13 - Interruptions (days) - How many hours per day do you experience water interruption?AWTP-14 - Factors affecting Alt Sources - Cost-effectivenessAWTP-14 - Factors affecting Alt Sources - Reliability (consistent supply)AWTP-14 - Factors affecting Alt Sources - Water Quality (Clean and safe)AWTP-14 - Factors affecting Alt Sources - Costumer service and maintenance supportAWTP-14 - Factors affecting Alt Sources - OthersAWTP-14 - Factors affecting Alt Sources - If others, input hereAWTP-15 - Desalination Awareness - YesAWTP-15 - Desalination Awareness - NoAWTP-16 - Desalinated Willingness - YesAWTP-16 - Desalinated Willingness - NoAWTP-17 - Desalinated Premium Pay - YesAWTP-17 - Desalinated Premium Pay - NoAWTP-17 - Desalinated Premium Pay - Depends on the cost differenceAWTP-17 - Desalinated Premium Pay - If yes, in what payment structureAWTP-17 - Desalinated Premium Pay - Fixed tariffAWTP-17 - Desalinated Premium Pay - Tiered pricingAWTP-17 - Desalinated Premium Pay - Pay per useAWTP-17 - Desalinated Premium Pay - SeasonalAWTP - Max Price - Commercial - 160AWTP - Max Price - Commercial - 200AWTP - Max Price - Commercial - 100AWTP-19 - Max Price - Commercial - OthersAWTP-19 - Max Price - Residential - 50AWTP-19 - Max Price - Residential - 80AWTP-19 - Max Price - Residential - 100AWTP-19 - Max Price - Residential - OthersAWTP-20 - Budget - < 3000AWTP-20 - Budget - 3000 - 6000AWTP-20 - Budget - 6000 - 9000AWTP-20 - Budget - 9000 - 15000AWTP-20 - Budget - OthersAWTP-21 - Concerns
0BI-1RBARANGAY IResidential07/06/2025Mary Josie FontanilllaResidentialRoss CarinoZone 5 Lubrin HeightsNaNNaNNaNNaNNaN6.001100.000000.000.0Pump Well00010.0NaN20.00NaNNaNNaNNaNNaNNaNFebJune00000600NaN0NaN00100000FALSE1Not enough water in well1001000000NaN0100000000NaN010NaN0000000NaN0000NaNOnly Summer00100NaN1010001NaN0010000NaN0.00.00.0NaN0000600NaN
1BI-2RBARANGAY IResidential07/06/2025Mary Josie FontanilllaResidentialRam GarciaNaNNaNNaNNaNNaNNaN5.001100.000011.000.0NaN00010.0NaN24.00NaNNaNNaN15.0NaNNaNMarchJune0000018028000NaN01000000FALSE0NaN1000000001Nauubos ang tubig sa poso0010000010NaN010NaN0000001Malinis0010NaNnatyanan11100NaN1010100NaN0101001NaN0.00.01.0NaN1000NaNNaN
2BI-3RBARANGAY IResidential07/06/2025Mary Josie FontanilllaResidentialSharie Jane AbandoNaNNaNNaNNaNNaNNaN2.001100.000010.000.0NaN00010.0NaN12.00NaNNaNNaNNaNNaNSharingMarchJune00000360NaN0NaN00100000FALSE1Walang gripo0100000001Tag-init lang0100000000NaN010NaN0000000NaN0000NaNTag-init lang11100NaN0110101NaN0000100NaN0.01.00.0NaN0000NaNNaN
3BI-4RBARANGAY IResidential07/06/2025Mary Josie FontanilllaResidentialMalidale MunaNaNNaNNaNNaNNaNNaN5.001100.000010.000.0NaN00010.0NaN6.63NaNNaNNaNNaNNaNSharingMarchJune00000120NaN0NaN01000000FALSE0NaN0100000000NaN0100000000NaN010NaN0000000NaN1000NaNNaN11000NaN1010000NaN0000000NaN0.00.00.0NaN0000500Mabuti
4BI-5RBARANGAY IResidential07/06/2025Mary Josie FontanilllaResidentialLovely ColetNaNNaNNaNNaNNaNNaNNaN11100.000010.000.0Sharing00010.0NaN12.00NaNNaNNaNNaNNaNSharingMarchJune00000360NaN0NaN01000000FALSE0NaN0100000000NaN0100000000NaN010NaN0000000NaN0000NaNWala11100NaN1010001NaN0000100NaN1.00.00.0NaN0000NaNNaN
5BI-6RBARANGAY IResidential07/06/2025Mary Josie FontanilllaResidentialValencia Jay-rNaNNaNNaNNaNNaNNaN5.011100.000001.000.0NaN00010.0NaN6.63NaNNaNNaNNaNNaNSharingFebJune01000NaNNaN0NaN01000000FALSE0NaN0100000000NaN0100000000NaN010NaN0000000NaN0000NaNWala11100NaN1010101NaN0010001NaN0.00.01.0NaN1000NaNNaN
6BI-7RBARANGAY IResidential08/06/2025Mary Josie FontanilllaResidentialMila ANchetaNaNNaNNaNNaNNaNNaN4.011100.000001.000.0NaN00010.0NaN12.70NaNNaNNaNNaNNaNNaNMarchJune01000NaN14000NaN10000000FALSE0NaN1000000001By Schedule0100000000NaN001NaN0000000NaN0010NaNNaN00100NaN1010100NaN0010100NaN1.00.00.0NaN0000500NaN
7BI-8RBARANGAY IResidential08/06/2025Mary Josie FontanilllaResidentialAlejo DucusinNaNNaNNaNNaNNaNNaN6.011100.000010.000.0NaN00010.0NaN8.00NaNNaNNaNNaNNaNNaNMarchMarch00000240NaN0NaN01000000FALSE0NaN0100000000NaN0010000000NaN010NaN0000000NaN0000NaNNaN00100NaN0110001NaN0000000NaN0.00.00.0NaN0000NaNFree water delivery in summer
8BI-9RBARANGAY IResidential08/06/2025Mary Josie FontanilllaResidentialManuel CaugaNaNNaNNaNNaNNaNNaN4.011100.000010.000.0NaN00000.0NaN16.00NaNNaNNaNNaNNaNNaNMarchMay00000480NaN0NaN01000000FALSE0NaN0100000000NaN0100000000NaN010NaN0000000NaN1000NaNNaN00100NaN1010001NaN0000001NaN0.00.00.0NaN0000NaNNot sure kasi may laman pa ang pump well
9BI-10RBARANGAY IResidential08/06/2025Mary Josie FontanilllaResidentialLarry CanonoBrgy INaNNaNNaNNaNNaN4.011100.000010.000.0NaN00010.0NaN12.00NaNNaNNaNNaNNaNNaNMarchJune00000360NaN0NaN01000000FALSE0NaN0100000000NaN0010000000NaN010NaN0000000NaN1000NaNNaN01100NaN1010001NaN0000001NaN0.00.01.0NaN0000Depends on everyday consumeKung maganda, okay.
GI - CodeBarangayClassificationGI - DateGI - InterviewerGI - Remarks (Small, Medium, Large, Residential)GI - Business Name/Residential Name/Contact numberGI - Business Location/AddressGI - Type of BusinessGI - No. of EmployeesGI - Operating Hours - StartGI - Operating Hours - EndCWSU-1 - Usage - SpecifyGI - MembersCWSU-1 - Usage - DrinkingCWSU-1 - Usage - Food preparationCWSU-1 - Usage - Cleaning & SanitationCWSU-1 - Usage - Manufacturing/ProductionCWSU-1 - Usage - Landscaping/IrrigationCWSU-1 - Usage - OthersCWSU-2 - TreatmentCWSU-3 - Current Sources - Tap Water (Water District etc.)CWSU-3 - Current Sources - Deep Well (owned)CWSU-3 - Current Sources - Truck Delivery Services (5 m3)CWSU-3 - Current Sources - Bottled water (5 gallons)CWSU-3 - Current Sources - OthersCWSU-3 - Current Sources - (If others, please specify)CWSU-4 - Primary Drinking Source - Tap Water (Water District etc.)CWSU-4 - Primary Drinking Source - Deep Well (owned)CWSU-4 - Primary Drinking Source - Truck Delivery Services (5 m3)CWSU-4 - Primary Drinking Source - Bottled water (5 gallons)CWSU-4 - Primary Drinking Source - OthersCWSU-4 - Primary Drinking Source - (If others, please specify)CWSU-4 - Primary Drinking Source - galsCWSU-5 - Ave Demand (in cbm)CWSU-6 - Actual Use (m3 / gal) - Tap Water (Water District etc.)CWSU-6 - Actual Use (m3 / gal) - Deep Well (owned)CWSU-6 - Actual Use (m3 / gal) - Truck Delivery Services (5 m3)CWSU-6 - Actual Use (m3 / gal) - Bottled water (5 gallons)CWSU-6 - Actual Use (m3 / gal) - Others (gallons)CWSU-7 - Peak Month - StartCWSU-7 - Peak Month - EndCWSU-8 - Monthly Costs - Less than PhP 1,000CWSU-8 - Monthly Costs - PhP 1,000 - PhP 3,000CWSU-8 - Monthly Costs - PhP 3,000 - PhP 5,000CWSU-8 - Monthly Costs - PhP 5,000 - PhP 10,000CWSU-8 - Monthly Costs - PhP 10,000 and aboveCWSU-8 - Monthly Costs - Water BottlesCWSU-8 - Monthly Costs - Truck DeliveryCWSU-8 - Monthly Costs - Others:CWSU-8 - Monthly Costs - If others, input hereCWSU-9 - Issues (Tap Water) - YesCWSU-9 - Issues (Tap Water) - NoCWSU-9 - Issues (Tap Water) - N/ACWSU-9 - Issues (Tap Water) - Supply interruptionCWSU-9 - Issues (Tap Water) - SalinityCWSU-9 - Issues (Tap Water) - TasteCWSU-9 - Issues (Tap Water) - SmellCWSU-9 - Issues (Tap Water) - TurbidityCWSU-9 - Issues (Tap Water) - CostsCWSU-9 - Issues (Tap Water) - OthersCWSU-9 - Issues (Tap Water) - If others, input hereCWSU-10 - Issues (Deep Well) - YesCWSU-10 - Issues (Deep Well) - NoCWSU-10 - Issues (Deep Well) - N/ACWSU-10 - Issues (Deep Well) - Supply interruptionCWSU-10 - Issues (Deep Well) - SalinityCWSU-10 - Issues (Deep Well) - TasteCWSU-10 - Issues (Deep Well) - SmellCWSU-10 - Issues (Deep Well) - TurbidityCWSU-10 - Issues (Deep Well) - CostsCWSU-10 - Issues (Deep Well) - OthersCWSU-10 - Issues (Deep Well) - If others, input hereCWSU-11 - Issues (Truck Water) - YesCWSU-11 - Issues (Truck Water) - NoCWSU-11 - Issues (Truck Water) - N/ACWSU-11 - Issues (Truck Water) - Supply interruptionCWSU-11 - Issues (Truck Water) - SalinityCWSU-11 - Issues (Truck Water) - TasteCWSU-11 - Issues (Truck Water) - SmellCWSU-11 - Issues (Truck Water) - TurbidityCWSU-11 - Issues (Truck Water) - CostsCWSU-11 - Issues (Truck Water) - OthersCWSU-11 - Issues (Truck Water) - If others, input hereCWSU-12 - Issues (Bottled Water) - YesCWSU-12 - Issues (Bottled Water) - NoCWSU-12 - Issues (Bottled Water) - N/ACWSU-12 - Issues (Bottled Water) - If yes, select all that applyCWSU-12 - Issues (Bottled Water) - Supply interruptionCWSU-12 - Issues (Bottled Water) - SalinityCWSU-12 - Issues (Bottled Water) - TasteCWSU-12 - Issues (Bottled Water) - SmellCWSU-12 - Issues (Bottled Water) - TurbidityCWSU-12 - Issues (Bottled Water) - CostsCWSU-12 - Issues (Bottled Water) - OthersCWSU-12 - Issues (Bottled Water) - If others, input hereCWSU-13 - Interruptions (days) - We have continuous water supply everydayCWSU-13 - Interruptions (days) - Once a weekCWSU-13 - Interruptions (days) - Twice a weekCWSU-13 - Interruptions (days) - Thrice a weekCWSU-13 - Interruptions (days) - OthersCWSU-13 - Interruptions (days) - How many hours per day do you experience water interruption?AWTP-14 - Factors affecting Alt Sources - Cost-effectivenessAWTP-14 - Factors affecting Alt Sources - Reliability (consistent supply)AWTP-14 - Factors affecting Alt Sources - Water Quality (Clean and safe)AWTP-14 - Factors affecting Alt Sources - Costumer service and maintenance supportAWTP-14 - Factors affecting Alt Sources - OthersAWTP-14 - Factors affecting Alt Sources - If others, input hereAWTP-15 - Desalination Awareness - YesAWTP-15 - Desalination Awareness - NoAWTP-16 - Desalinated Willingness - YesAWTP-16 - Desalinated Willingness - NoAWTP-17 - Desalinated Premium Pay - YesAWTP-17 - Desalinated Premium Pay - NoAWTP-17 - Desalinated Premium Pay - Depends on the cost differenceAWTP-17 - Desalinated Premium Pay - If yes, in what payment structureAWTP-17 - Desalinated Premium Pay - Fixed tariffAWTP-17 - Desalinated Premium Pay - Tiered pricingAWTP-17 - Desalinated Premium Pay - Pay per useAWTP-17 - Desalinated Premium Pay - SeasonalAWTP - Max Price - Commercial - 160AWTP - Max Price - Commercial - 200AWTP - Max Price - Commercial - 100AWTP-19 - Max Price - Commercial - OthersAWTP-19 - Max Price - Residential - 50AWTP-19 - Max Price - Residential - 80AWTP-19 - Max Price - Residential - 100AWTP-19 - Max Price - Residential - OthersAWTP-20 - Budget - < 3000AWTP-20 - Budget - 3000 - 6000AWTP-20 - Budget - 6000 - 9000AWTP-20 - Budget - 9000 - 15000AWTP-20 - Budget - OthersAWTP-21 - Concerns
490TAN-7CTANQUICommercial06/06/2025Jeanette CorpuzSmallBURRITO FOOD HUBTanquiRestaurant / Food Service38:0017:00NaNNaN01111.001010.010.0NaN00010.0NaN60.0NaNNaNNaN6065NaNNaNNaN000001200NaN110001000100000NaN1001110000NaN0010000000NaN010NaN0000000NaN0100NaNNaN11100NaN1010100NaN0000100NaNNaNNaNNaNNaN1000NaNNaN
491TAN-8CTANQUICommercial06/06/2025Jeanette CorpuzSmallR&R EATERYTanquiRestaurant / Food Service78:0017:00NaNNaN11110.000001.000.0NaN00100.0NaN91.524.00000NaNNaNNaNNaN200NaNNaN00100NaNNaN0NaN0000000000NaN0100000000NaN0010000000NaN010NaN0000000NaN1000NaNNaN10000NaN0101001NaN0000010NaNNaNNaNNaNNaN1000NaNDecrease sa bill
492TAN-9CTANQUICommercial06/06/2025Jeanette CorpuzLargeAQUAERUM WATER REFILLING STATIONTanquiRetail48:0017:00NaNNaN10100.001110.010.0NaN11010.0NaN91.5NaNNaNNaNNaNNaNNaNNaNNaN00000NaNNaN0NaN0100000000NaN0100000000NaN0100000000NaN010NaN0000000NaN0000NaNNaN11110NaN0110010NaN0000000NaNNaNNaNNaNNaN0000NaNNaN
493TAN-10CTANQUICommercial06/06/2025Jeanette CorpuzSmallMasarap John CanteenTanquiNaN38:0017:00NaNNaN01100.001010.000.0NaN01000.0NaN91.5NaNNaNNaNNaNNaNNaNOctoberDecember00100NaNNaN0NaN0010000000NaN1000000001konti0010000000NaN001NaN0000000NaN1000NaNNaN01000NaN1010010NaN0000000NaNNaNNaNNaNNaN1000NaNNaN
494TAN-11CTANQUICommercial06/06/2025Jeanette CorpuzMediumEZ POINT RESTO BAR/ GERRY'S RESTO BARTanquiRestaurant / Food Service40:0023:59NaNNaN11110.001101.010.0NaN10000.0NaN91.51.70325NaNNaNNaN15NaNNaNNaN01000NaNNaN0NaN1000000000NaN0100000000NaN0100000000NaN010NaN0000000NaN0000NaNnawawala tapos meron kinabukasan11110NaN1001010NaN0000100NaNNaNNaNNaNNaN1000NaNNaN
495TAN-12CTANQUICommercial06/09/2025Jeanette CorpuzSmallLABHAN KITA LAUNDROMATTanquiNaN58:0017:00NaNNaN11100.001010.000.0NaN01000.0NaN91.5NaNNaNNaNNaNNaNNaNMarchMay10000NaNNaN0NaN0100000000NaN0100000000NaN0100000000NaN010NaN0000000NaN0000NaNNaN11100NaN0110000NaN0000000NaNNaNNaNNaNNaN1000NaNNaN
496TAN-13CTANQUICommercial06/09/2025Jeanette CorpuzLargeNORTH CENTRAL SCHOOLTanquiNaNNaN8:0017:00NaNNaN11100.001010.010.0NaN00001.0water refilling91.5NaNNaNNaNNaNNaNNaNNaNNaN00000NaNNaN0NaN0000000000NaN1000001000NaN0010000000NaN100NaN0000010NaN0100NaNNaN00010NaN0110001NaN0000000NaNNaNNaNNaNNaN1000NaNNaN
497TAN-14CTANQUICommercial06/08/2025Jeanette CorpuzLargeMDL LAUDNRY SHOP & WATER STATIONTanquiRetail28:0017:00NaNNaN11111.001010.000.0NaN11010.0NaN91.5NaNNaNNaNNaNNaNNaNNaNNaN00000NaNNaN0NaN0100000000NaN0100000000NaN0100000000NaN010NaN0000000NaN0000NaNNaN11110NaN0101010NaN0000000NaNNaNNaNNaNNaN0000NaNNaN
498TAN-15CTANQUICommercial06/08/2025Jeanette CorpuzNaNMUCHAS GRACIAS CATERING SERVICESTanquiRestaurant / Food Service18:0017:00NaNNaN11100.000100.011.0NaN00011.0Purified91.5NaNNaNNaNNaNNaNNaNNaNNaN00000NaNNaN0NaN0100000000NaN0010000000NaN0010000000NaN010NaN0000000NaN1000NaNNaN00100NaN0110001NaN0000000NaNNaNNaNNaNNaN0000NaNNaN
499TAN-16CTANQUICommercial06/09/2025Jeanette CorpuzLargeSAN FERNANDO SOUTH CENTRAL INTEGRATED SCHOOLTanquiCollege / University958:0017:00NaNNaN11100.000110.000.0NaN10000.0NaN91.53.40650NaNNaNNaN30NaNMarchApril00100NaNNaN0NaN1001000000NaN1001010000NaN0100000000NaN010NaN0000000NaN1000NaNNaN11100NaN1010001NaN0000100NaNNaNNaNNaNNaN1000NaNNaN